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Recent advancements in deep learning have significantly improved the performance of remote sensing scene classification, a critical task in remote sensing applications. This study presents a new aerial scene classification model, the Multi-Scale Swin–CNN Aerial Classifier (MSCAC), which employs the Swin Transformer, an advanced architecture that has demonstrated exceptional performance in a range of computer vision applications. The Swin Transformer leverages shifted window mechanisms to efficiently model long-range dependencies and local features in images, making it particularly suitable for the complex and varied textures in aerial imagery. The model is designed to capture intricate spatial hierarchies and diverse scene characteristics at multiple scales. A framework is developed that integrates the Swin Transformer with a multi-scale strategy, enabling the extraction of robust features from aerial images of different resolutions and contexts. This approach allows the model to effectively learn from both global structures and fine-grained details, which is crucial for accurate scene classification. The model's performance is evaluated on several benchmark datasets, including UC-Merced, WHU-RS19, RSSCN7, and AID, where it demonstrates a superior or comparable accuracy to state-of-the-art models. The MSCAC model's adaptability to varying amounts of training data and its ability to improve with increased data make it a promising tool for real-world remote sensing applications. This study highlights the potential of integrating advanced deep-learning architectures like the Swin Transformer into aerial scene classification, paving the way for more sophisticated and accurate remote sensing systems. The findings suggest that the proposed model has significant potential for various remote sensing applications, including land cover mapping, urban planning, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Aim: The main objective of this work is to propose an efficient segmentation model for accurate and robust lung segmentation from computed tomography (CT) images, even when the lung contains abnormalities such as juxtapleural nodules, cavities, and consolidation. Methodology: A novel deep learning‑based segmentation model, pyramid‑dilated dense U‑Net (PDD‑U‑Net), is proposed to directly segment lung regions from the whole CT image. The model is integrated with pyramid‑dilated convolution blocks to capture and preserve multi‑resolution spatial features effectively. In addition, shallow and deeper stream features are embedded in the nested U‑Net structure at the decoder side to enhance the segmented output. The effect of three loss functions is investigated in this paper, as the medical image analysis method requires precise boundaries. The proposed PDD‑U‑Net model with shape‑aware loss function is tested on the lung CT segmentation challenge (LCTSC) dataset with standard lung CT images and the lung image database consortium‑image database resource initiative (LIDC‑IDRI) dataset containing both typical and pathological lung CT images. Results: The performance of the proposed method is evaluated using Intersection over Union, dice coefficient, precision, recall, and average Hausdorff distance metrics. Segmentation results showed that the proposed PDD‑U‑Net model outperformed other segmentation methods and achieved a 0.983 dice coefficient for the LIDC‑IDRI dataset and a 0.994 dice coefficient for the LCTSC dataset. Conclusions: The proposed PDD‑U‑Net model with shape‑aware loss function is an effective and accurate method for lung segmentation from CT images, even in the presence of abnormalities such as cavities, consolidation, and nodules. The model’s integration of pyramid‑dilated convolution blocks and nested U‑Net structure at the decoder side, along with shape‑aware loss function, contributed to its high segmentation accuracy. This method could have significant implications for the computer‑aided diagnosis system, allowing for quick and accurate analysis of lung regions. [ABSTRACT FROM AUTHOR]

Aims: Deep-learning methods are becoming versatile in the field of medical image analysis. The hand-operated examination of smaller nodules from computed tomography scans becomes a challenging and time-consuming task due to the limitation of human vision. A standardized computer-aided diagnosis (CAD) framework is required for rapid and accurate lung cancer diagnosis. The National Lung Screening Trial recommends routine screening with low-dose computed tomography among high-risk patients to reduce the risk of dying from lung cancer by early cancer detection. The evolvement of clinically acceptable CAD system for lung cancer diagnosis demands perfect prototypes for segmenting lung region, followed by identifying nodules with reduced false positives. Recently, deep-learning methods are increasingly adopted in medical image diagnosis applications. Subjects and Methods: In this study, a deep-learning-based CAD framework for lung cancer diagnosis with chest computed tomography (CT) images is built using dilated SegNet and convolutional neural networks (CNNs). A dilated SegNet model is employed to segment lung from chest CT images, and a CNN model with batch normalization is developed to identify the true nodules from all possible nodules. The dilated SegNet and CNN models have been trained on the sample cases taken from the LUNA16 dataset. The performance of the segmentation model is measured in terms of Dice coefficient, and the nodule classifier is evaluated with sensitivity. The discriminant ability of the features learned by a CNN classifier is further confirmed with principal component analysis. Results: Experimental results confirm that the dilated SegNet model segments the lung with an average Dice coefficient of 0.89 ± 0.23 and the customized CNN model yields a sensitivity of 94.8 on categorizing cancerous and noncancerous nodules. Conclusions: Thus, the proposed CNN models achieve efficient lung segmentation and two-dimensional nodule patch classification in CAD system for lung cancer diagnosis with CT screening. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC. [ABSTRACT FROM AUTHOR]