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Whole brain segmentation from magnetic resonance images (MRI) is crucial in diagnosing brain diseases and analyzing neuroimaging data. Despite advances through deep learning, challenges such as uneven gray distribution and the presence of artifacts still present hurdles in medical image processing. These limitations are often a result of insufficient spatial contextual information and lack of attention to important regions within existing models. To address these issues, this paper presents SSA‐UNet (Squeeze‐and‐Excitation and Self‐Attention UNet), a uniquely designed deep convolutional neural network that integrates spatial constraints by converting three consecutive 2D MRI slices into a single 2.5D image. This facilitates capturing inter‐slice dependencies effectively. Additionally, the newly formulated SSA block, which sequentially incorporates channel attention and Self‐Attention mechanisms, is placed before the decoders in the conventional U‐Net architecture. This enables the network to automatically weight different feature maps and focus more effectively on regions requiring precise segmentation. Rigorous evaluations on LPBA40 and IBSR18 datasets substantiate the remarkable improvements in accuracy and stability achieved by SSA‐UNet. Results indicate Dice coefficients of 98.38% and 97.47%, specificity of 99.69% and 99.57%, and sensitivity of 98.5% and 97.98% for the respective datasets. Compared to other existing models, SSA‐UNet shows significant improvements on both the LPBA40 and IBSR18 datasets. On the LPBA40 dataset, SSA‐UNet's Dice coefficient improved by 0.33% compared to the sub‐optimal model, while on the IBSR18 dataset, the improvement reached 1.78%. These empirical findings demonstrate SSA‐UNet's heightened capability in addressing the long‐standing challenges in MRI‐based whole‐brain segmentation. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural network (CNN) has shown great power in single image super resolution (SISR) reconstruction, and achieving significant improvements over traditional methods. Despite the great success of these CNN‐based methods, direct application of these methods to some edge devices is impractical due to the large computational overhead required. To address this problem, a novel, lightweight SISR network focusing on speed and accuracy, called the multi‐path feedback fusion network (MFFN), has been designed in this paper. Specifically, in order to extract features more effectively, the authors propose a novel fusion attention feedback block (FAFB) as the main building block of MFFN. The FAFB consists of a backbone branch and several hierarchical branches. The backbone branch is composed of stacked enhanced pixel attentional blocks (EPAB), which are responsible for incremental deep feature learning on the feature map. And the hierarchical branches are responsible for extracting feature maps with different sizes of receptive fields and fusing these feature maps with the features extracted from the trunk branches to achieve multi‐scale feature learning, which the authors refer to this design as the multi‐scale fusion block (MSFB). Extra enhancement information (EIE) is added to each EPAB input, which enables the backbone branch to learn more effectively. On the other hand, the outputs of the cascade branches are further complemented by an additional feedback fusion enhancement block (FFEB) before being fused with the output of the trunk branches to achieve more comprehensive and accurate feature learning. Numerous experiments have shown that MFFN achieves higher accuracy than other state‐of‐the‐art methods on benchmark test sets. [ABSTRACT FROM AUTHOR]

Lightweight deep learning models for face recognition are becoming increasingly crucial for deployment on resource‐constrained devices such as embedded systems or mobile devices. This paper presents a highly efficient and compact deep learning (DL) model that achieves state‐of‐the‐art performance on various face recognition benchmarks. The developed DL model employs one‐ or few‐shot learning to obtain effective feature embeddings and draws inspiration from FaceNet with significant refinements to achieve a memory size of only 3.5 MB—about 30 times smaller than FaceNet—while maintaining high accuracy and real‐time performance. The study demonstrates the model's effectiveness through extensive experiments, which include testing on public datasets and the model's ability to recognize occluded faces in uncontrolled environments using grayscale input images. Compared to the state‐of‐the‐art lightweight models, the proposed model requires fewer FLOPs (0.06G), has a smaller number of parameters (1.2 M), and occupies a smaller model size (3.5 MB) while achieving a competitive level of recognition accuracy and real‐time performance. The results show that the model is well‐suited for deployment in embedded domains, including live entrance security checks, driver authorization, and in‐class attendance systems. The entire code of FN8 is available onGitHub. [ABSTRACT FROM AUTHOR]

Great progress has been made in deep learning image reconstruction, but there are still problems that need to be improved, such as the extraction of image texture details and the improvement of the overall contour quality of the image, and how to reduce the transmission cost is also the focus of research. This paper proposes a fast bilateral network suitable for both grayscale and color image reconstruction. In the compression network, bilinear interpolation, fully connected layer and convolutional neural network are selected for image compression. The reconstruction network is divided into two parts: the texture path and contour path, the former reconstructs the remaining texture details of the image, and the latter performs deep contour reconstruction. The bilateral complementary residual connection method transfers the texture information to the contour path and improves the contour quality, and the improvement of the contour quality can improve the learning of texture details. Through a large number of data tests, it shows that the network in this paper has achieved comparable or better results in terms of image reconstruction quality, time‐consuming and robustness against noise. It solves the problem of memory consumption for storing a large number of images, and also provides convenience for shortening space and time during image transmission. [ABSTRACT FROM AUTHOR]

Automated breast ultrasound (ABUS) imaging system is a practical technique to automatically scan the whole breast. Automatic tumor detection plays a significant role in the clinic. However, training deep convolutional neural networks (CNNs) for tumor detection needs a large quantity of labeled data. It is time‐consuming and expensive to manually annotate tumor positions in ABUS images. In this paper, a novel semi‐supervised learning EfficientDet (SSL‐E) model is proposed for ABUS tumor detection. Our SSL‐E model solves the tumor detection problem from high similarity and serious unbalance between tumors and backgrounds. Considering the image contrast variation and tumor scale variations in ABUS images, color transformation and geometric transformation are employed for data augmentations. Then the consistency between image and its augmented version is developed, thus the robustness of the detector can be improved. Aiming at the problem of serious unbalance between tumors and backgrounds, a novel copy‐paste synthesis strategy is designed, which can generate more tumor samples and enhance tumor diversity. This method is tested on 68 tumor volumes and 68 normal volumes, including 43,248 slices (1683 tumor slices and 41,565 normal slices). It obtains a promising result with sensitivity of 90.2% and false positives per image (FPs/I) at 0.15. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches. [ABSTRACT FROM AUTHOR]

Different from most current studies using convolutional neural network (CNN), a deep learning detection method for steel plate surface defects based on Transformer is researched. This paper presents a process, network structure and detection method for steel strip surface defect detection. A Swin Transformer was used to extract hierarchical features in the detection system. Then feature pyramid networks (FPN) was used to fuse the above features to form multi‐scale feature maps, and region proposal network (RPN) was adopted to generate the generate region of interest (ROI) of defects. Finally, the ROI head was used to generate defect category information and its precise location. Here, ablation experiments were conducted to explore the impact of different backbone networks and the number of stages of Swin Transformer and FPN on target detection performance to prove the rationality and efficiency of the network. On the NEU‐DET dataset, the proposed algorithm achieved 81.1% mAP, which is higher than that of classic CNN detection methods such as Faster R‐CNN, SSD, Yolo v3 and RepPoints. [ABSTRACT FROM AUTHOR]

In this paper, a large‐scale dataset called the Iran Autonomous Driving Dataset (IADD) is presented, aiming to improve the generalization capability of the deep networks outside of their training domains. The IADD focuses on 2D object detection and contains more than 97,000 annotated images, covering six common object classes in the field of autonomous vehicles. To improve the generalization of the models, a wide variety of driving conditions and domains, including the city and suburban road settings, adverse weather conditions, and various traffic flows, are presented in the IADD images. The results of exhaustive evaluations conducted on several state‐of‐the‐art convolutional neural networks reveal that not only the trained architectures have performed successfully on test data of the IADD, but also they have upheld high precision in the assessments of generalization capability. In order to challenge the models, broad range of simulations have been performed in the CARLA software environment; which due to the synthetic nature of the simulated images, severe domain shifts have been observed between the CARLA and the IADD. Also, the cross‐domain evaluation results have confirmed the efficacy of the IADD in enhancing the generalization ability of the deep learning models. The dataset is available in: https://github.com/ahv1373/IADD. [ABSTRACT FROM AUTHOR]

This paper introduce an efficient object detection network named Trident‐You Only Look Once (YOLO), which is designed for mobile devices with limited computing power. The new architecture is improved based on YOLO v4‐tiny. The authors redesign the network structure and propose a trident feature pyramid network (Trident‐FPN), which can improve the precision and recall of lightweight object detection. Specifically, Trident‐FPN increases the computational complexity by only a small amount of floating point operations per second (FLOPs) and obtains a multi‐scale feature map of the model, which significantly lightweight object detection performance. To enlarge the receptive field of the network with the fewest FLOPs, this paper redesign the receptive field block (RFB) and spatial pyramid pooling (SPP) layer and propose tinier cross‐stage partial RFBs and smaller cross‐stage partial SPPs. This paper present extensive experiments, and Trident‐YOLO shows strong performance compared to that of other popular models on the PASCAL VOC and MS COCO. On the MS COCO and PASCAL VOC 2007 test sets, the mean average precision (mAP) of Trident‐YOLO improved by 4.5% and 5.0%, respectively. Trident‐YOLO also reduce the network size by more than 54.4% compared to YOLO v4‐tiny. With a 23.7% FLOP reduction, the FPS is improved by 1.9 on an Nvidia Jetson Xavier NX. [ABSTRACT FROM AUTHOR]

In the process of detecting materials inside a tray in an automated production line, it is necessary to detect not only the known materials and blank space in the designated area, but also the unknown materials misplaced inside the tray. However, the supervised detection algorithm based on deep learning can only detect the known and blank material areas. Therefore, this paper proposed a phased material detection. The first stage is to detect the tray and then identify the material area in the second stage. In order to improve the tray detection accuracy during the first stage under the condition of a high intersection ratio, an improved YOLOv5s tray detection method is proposed. The structure of YOLOv5s is improved using the SENet. This paper proposes to use the rich geometric information of the shallow network and the high‐level semantic information to integrate the bypass features. MAP@0.5:0.95 of the improved model increased from 95.7% to 96.6% and MAP@0.95 from 78.5% to 90.8%. The challenge of detecting unknown wrong materials on a tray can be resolved through the recognition of material area segmentation images processed by using the improved pre‐detection algorithm, together with the relative position reference between the material area and the tray. The experimental results showed that the improved method proposed meets the industrial detection requirements with an overall recognition accuracy of 91% within a 250 ms detection interval. [ABSTRACT FROM AUTHOR]

Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods. [ABSTRACT FROM AUTHOR]

Iris segmentation is an important step in the process of iris recognition. Iris images collected under non‐cooperative conditions always contain various noise, which is a challenge for iris segmentation. Most U‐Net‐based methods have made great achievements in iris segmentation. However, this architecture lacks of focusing on target structures of varying shapes, and robustness in segmenting objects with significant shape variations. In this paper, we propose RAG‐Net: an efficient iris segmentation method based on deep learning. In contrast to many previous convolutional neural network (CNN)‐based iris segmentation methods, we adopted the attention gate (AG) mechanism and ResNet‐50 in the U‐Net architecture to improve iris segmentation accuracy, the AG module was included in the skip connection part of the RAG‐Net architecture to further identify salient feature regions and prune feature responses, which preserve only the activations relevant to the required information, and the ResNet‐50 module was used to improve the robustness of the segmentation performance. Using this model, efficient iris segmentation in a non‐cooperative environment can be realized. The proposed method was trained and evaluated using the CASIA.v4‐distance, CASIA.v4‐thousand, UBIRIS.v2, and MICHE‐I databases. From the view of the segmentation results, the proposed RAG‐Net is one of effective architecture in iris segmentation methods. [ABSTRACT FROM AUTHOR]

Crack detection of the concrete bridge is an essential index for the safety assessment of bridge structure. It is more important to check the whole structure than to check the accuracy in the damage assessment. However, the traditional deep learning model method cannot completely detect the crack structure, which challenges image‐based crack detection. For this reason, we propose deep bridge crack classification (DBCC)‐Net as a classification‐based deep learning network. By pruning the Yolox, the regression problem of the target detection is converted to the binary classification problem to avoid the network performance degradation caused by the translation invariance of the convolutional neural network (CNN). In addition, the network post‐processing and a two‐stage crack detection strategy are proposed to enable the network to detect cracks and extract crack morphology in high‐resolution images quickly. In the first stage, DBCC‐Net realizes the coarse extraction of crack position based on image slice classification. In the second stage, the complete crack morphology is extracted from the location suggested by the semantic segmentation network. Experimental results show that the proposed two‐stage method has 19 frames per second (FPS) and 0.79 Miou (mean intersection over union) at the actual bridge images with 2560×2560 pixels. Although FPS is reduced, the Miou value is 7.8% higher than other methods, proving this paper's practical value. [ABSTRACT FROM AUTHOR]

This paper investigates accurate industry online scene text recognition techniques for metallic debossed characters (MDCs). As MDCs have low contrast with its background and easy to interfere with corrosion rust, oxidate skin or specular reflection and so forth, a multi‐scale image fusion algorithm is first proposed to restore the MDCs' shape and appearance information to enhance the sample's contrast by using the MDCs' depth characteristic with the help of four‐directional illumination. With this method, the three most important reflection‐related picture quality evaluation standards, namely contrast, saturation, and exposure, can be enhanced simultaneously. Next, based on the fusion result, a U‐shaped network based on VGG‐16 network architecture is used for text localization. In order to improve network receptive fields, an improved post‐processing output module that combines a text sequence score map for coarsening localization with a single‐character score map for fine localization is proposed, so that the network's adaptability and accuracy for long texts are improved. Finally, a convolutional recurrent neural network is adopted to realize the recognition of sequence characters. The advantages and effectiveness of the proposed method are statistically analyzed with the data from a liquefied petroleum gas cylinder annual periodic inspection production line. [ABSTRACT FROM AUTHOR]

This paper presents a method for categorizing tumour disease from magnetic resonance imaging images using a convolutional neural network. The proposed technique consists of three major phases, including feature extraction, feature selection, and combination. The authors considered the classification method using convolutional neural network without any pre‐processing on input images as the original method. The original method is then improved in some sequential phases when convolutional neural network uses features generated by feature extraction methods. Many popular feature extraction methods in generating the input features of the network are examined. After examining the results, a set of feature extraction schemes with appropriate performance are selected for the next phase. Also, the authors assigned weight factors to each of the selected methods, according to their accuracy. By assigning these weight factors to the methods, the network's accuracy increased to an acceptable level compared to the first and second observations. An accuracy above 99.76% was achieved, which is a 2% improvement using these feature extraction methods compared to the original method. The authors also added a third class named 'I do not know' to increase the tumour detection problem's reliability in the last phase. The authors have successfully introduced a convolutional neural network architecture with high accuracy and reliability. [ABSTRACT FROM AUTHOR]

The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recognising algorithms, such as limited receptive field, slow convergence rate and low accuracy of small particle recognition, this paper proposes a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN‐YOLOv3). To improve the accuracy of anchor frame positioning and enhance the diversity of the dataset, the deformed convolution YOLOv3 network model is established based on the detection algorithm YOLOv3, using deformable convolution, multiple k‐means clustering results average method and data enhancement technology as means. The model was trained through the self‐designed dataset, and the algorithm's correctness and accuracy for coal gangue recognition under different size and illumination conditions are verified. The test results showed that the algorithm effectively detects and recognises coal gangue, improves the accuracy and efficiency of detecting and recognising small‐size coal and gangue and improves environmental robustness. Furthermore, compared with the traditional recognition algorithm, the network convergence speed of this algorithm is significantly improved, the mAP is increased to 99.45%, and the maximum FLOPs value is reduced by 61.4%. Accordingly, this research is considered to be of certain theoretical value and technical reference for identifying coal gangue. [ABSTRACT FROM AUTHOR]

Deraining has been attracting a lot of attention from researchers, and various methods have been proposed, especially deep‐networks are widely adopted in recent years. Their structures and learning become more and more complicated and diverse, making it difficult to analyze the contributions and improvements. In this paper, a comprehensive review for current rain removal methods is first provided to show their contributions. Specifically, they are reviewed in terms of handing rain streaks and rain mist. Second, besides evaluating their rain removal ability, they are also evaluated in terms of their impact on subsequent stereo‐matching task. To this end, a new deraining dataset is first prepared, called Rain‐Kitti2012 and Rain‐Kitti2015. They are created by adding rain part to clean image‐pairs in Kitti2012 and Kitti2015. By then, nine state‐of‐the‐art deraining methods are evaluated with full‐reference and no‐reference image quality assessment metrics. Furthermore, the blurriness and distortion types introduced during deraining are measured. Finally, three learning‐based stereo matching methods are compared, and they take the outputs of deraining methods as inputs. It is further discussed how derained images influence the accuracy of stereo matching, which can provide some insight for jointly handling rain removal and stereo matching. 1: A comprehensive review for the current rain removal methods is provided. They are categorized into rain‐streak‐oriented and rain‐mist‐oriented approaches in terms of degradation type, and are categorized into model‐driven and data‐driven approaches in terms of methodology. 2: A new image deraining dataset is introduced, which is the first dataset that can be used to perform stereo‐matching‐driven evaluation for deraining methods. The dataset is created by adding rain part to clean images in KITTI2012 and KITTI2015. 3: We evaluate 9 deep learning based deraining methods with full‐reference and no‐ reference metrics. In addition, the types of distortions produced by these methods are discussed and measured quantitatively. And, the impact of 9 deraining methods on the subsequent stereo matching task is evaluated, which can provide some insight on how to design stereo matching task‐driven deraining methods. [ABSTRACT FROM AUTHOR]

With the performance increase of convolutional neural network (CNN), the disadvantages of CNN's high storage and high power consumption are followed. Among the methods mentioned in various literature, filter pruning is a crucial method for constructing lightweight networks. However, the current filter pruning method is still challenged by complicated processes and training inefficiency. This paper proposes an effective filter pruning method, which uses the saliency of the feature map (SFM), i.e. information entropy, as a theoretical guide for whether the filter is essential. The pruning principle use here is that the filter with a weak saliency feature map in the early stage will not significantly improve the final accuracy. Thus, one can efficiently prune the non‐salient feature map with a smaller information entropy and the corresponding filter. Besides, an over‐parameterized convolution method is employed to improve the pruned model's accuracy without increasing parameter at inference time. Experimental results show that without introducing any additional constraints, the effectiveness of this method in FLOPs and parameters reduction with similar accuracy has advanced the state‐of‐the‐art. For example, on CIFAR‐10, the pruned VGG‐16 achieves only a small loss of 0.39% in Top‐1 accuracy with a factor of 83.3% parameters, and 66.7% FLOPs reductions. On ImageNet‐100, the pruned ResNet‐50 achieves only a small accuracy degradation of 0.76% in Top‐1 accuracy with a factor of 61.19% parameters, and 62.98% FLOPs reductions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel image classification architecture named ensemble cross‐stage partial attention network based on the backbone network DarkNet53 of Yolov3 to improve the feature extraction capability and the interpretability of image classification. This network has multiple advantages, including light model parameters, fast classification speed, and high classification accuracy for small objects and complex images. Local network architectures of different cross‐phases are added in the proposed network structure to reduce the calculation. Furthermore, channel and hybrid domain attention modules, which, respectively, fuse the branch feature with the extracted channel and spatial attention features, are designed for feature extraction of images. Experimental results confirm the improved performance of the proposed approach on the CIFAR‐100, ImageNet, and UCMerced datasets. In addition, experiments on the MSCOCO dataset suggest the application of the proposed method to object detection with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

With increasing demand for scale‐invariant and fast object recognition, speeded up robust features (SURF) have emerged and become a widely used feature extraction algorithm in computer vision. Nevertheless, SURF still requires high memory usage and heavy computations caused by the keypoint detection procedure that produces huge image pyramid composed of many hessian determinant (HD) data for supporting the scale‐invariance. Therefore, in this paper, dynamic optimization schemes of the HD image pyramid are proposed for completely removing the redundancies of the keypoint detection with keeping the original functionality of SURF without any loss. The proposed approach has shown to reduce memory usage by 45–51%, the execution time by 37–50% and the power consumption by 18–42% when compared with the keypoint detection in the original SURF algorithm. [ABSTRACT FROM AUTHOR]

Change detection (CD) is a fundamental yet challenging problem, which aims at detecting changed object in two observations. Recent CD methods are designed based on the off‐the‐shelf semantic segmentation network architectures, which is not optimal for extracting and using change‐related features. In this paper, a novel CD network architecture is proposed, including change‐related feature extraction, cross feature enhancement, and multi‐level supervision. Absolute difference of the features of different convolutional layers is first computed from a Unet‐like network for two observations. The features are partitioned into high‐ and low‐level features according to their functionalities. Then the high‐ and low‐level features are recurrently refined by cross feature enhancement to increase the representational ability of the features. The network learns change‐related features with multi‐level supervisions. The final CD result can be obtained by fusing multiple predictions. Experimental results on three CD benchmark datasets indicate the superiority of the authors' method when compared with six state‐of‐the‐art deep learning‐based CD methods. [ABSTRACT FROM AUTHOR]

The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection. This paper proposes a novel method called multi‐receptive and parallel integration, for salient object detection. Firstly, a multi‐receptive enhancement module is designed to effectively expand the receptive fields of features from different layers and generate features with different receptive fields. Multi‐receptive enhancement module can enhance the semantic representation and improve the model's perception of the image context, which enables the model to locate the salient object accurately. Secondly, in order to reduce the reuse of redundant information in the complex top‐down fusion method and weaken the differences between semantic features, a relatively simple but effective parallel fusion strategy is proposed. It allows multi‐scale features to better interact with each other, thus improving the overall performance of the model. Experimental results on multiple datasets demonstrate that the proposed method outperforms state‐of‐the‐art methods under different evaluation metrics. [ABSTRACT FROM AUTHOR]

Medical science is a challenging area for various problems associated with health care and there always exists scope for continuous medical research. The major challenges in medical imaging are in the region of lesion, segmentation and classification of tumours in the brain. Several technical challenge exists in the classification due to the variation in the tumour size, shape, texture information and location. There is a need for automatic identification of high‐grade glioma (HGG) and lower‐grade glioma (LGG). The management and grade of brain tumour depend on the depth of the tumour. Due to its irregular features, manual segmentation involves longer time and also increases the misclassification rate. Inspired by these issues, this paper introduces two automatic deep learning networks called U‐Net‐based deep convolution network and U‐Net with dense network. The proposed method is evaluated in our own brain tumour image database consisting of 300 high‐grade brain tumour cases and 200 normal cases. To improve the overall efficiency of the network, data augmentation is applied in both training and validation. The proposed U‐Net‐based Dense Convolutional Network (DenseNet) architecture is compared with the performance of U‐Net architecture and concluded that the proposed DenseNet produces a higher dice value. The validation results have revealed that our proposed method can have better segmentation efficiency. Also, the performance of the proposed DenseNet achieved better results compared with the state‐of‐the‐art algorithms. Validation of the test images proves that segmented output classification of tumour risk and the normal region produces a sensitivity of 88.7%, Jaccard index of 0.839, dice score value of 0.911, F1 score of 0.906 and specificity of 100% using U‐Net‐based DenseNet architecture. [ABSTRACT FROM AUTHOR]

This paper intends to present an automated mango grading system under four stages (1) pre‐processing, (2) feature extraction, (3) optimal feature selection and (4) classification. Initially, the input image is subjected to the pre‐processing phase, where the reading, sizing, noise removal and segmentation process happens. Subsequently, the features are extracted from the pre‐processed image. To make the system more effective, from the extracted features, the optimal features are selected using a new hybrid optimization algorithm termed the lion assisted firefly algorithm (LA‐FF), which is the combination of LA and FF, respectively. Then, the optimal features are given for the classification process, where the optimized deep convolutional neural network (CNN) is deployed. As a major contribution, the configuration of CNN is fine‐tuned via selecting the optimal count of convolutional layers. This obviously enhances the classification accuracy in grading system. For fine‐tuning the convolutional layers in the deep CNN, the LA‐FF algorithm is used so that the classifier is optimized. The grading is evaluated on the basis of healthydiseased, ripeunripe and bigmediumvery big cases with respect to type I and type II measures and the performance of the proposed grading model is compared over the other state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]