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In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases. [ABSTRACT FROM AUTHOR]

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas. [ABSTRACT FROM AUTHOR]

Intelligent medical diagnosis and computing system faces many challenges in complex object recognition, large-scale data imaging and real-time diagnosis, such as poor real-time computing, low efficiency of data storage and low recognition rate of lesions. In order to solve the above problems, this paper proposes a medical intelligent computing system and a series of algorithms for the clinical pathology of cervical cancer based on the multi-scale imaging and transfer learning framework. Firstly, based on data dimensions, imaging errors and other factors, this paper designs a multi-scale time-sharing elastic imaging algorithm based on image reconstruction time and data sample characteristics. Then, taking the burst imaging cohort and the calculation data set of new cervical cancer cases as the objects, based on the difficulties of cervical cancer feature modeling, this paper proposes the transfer learning algorithm of clinical and pathological features of cervical cancer. Finally, a medical intelligent computing system for cervical cancer pathology analysis and calculation with high efficiency and reliability is established. A series of proposed algorithms are compared with single-scale Retinex (SSR), which is based on single-scale Retinex migration learning (SSR-TL). The experimental results show that the proposed algorithm in cervical cancer pathological imaging and scoring, as well as the feature extraction and recognition of lesions, especially the efficiency of system execution, is obviously due to the comparison algorithm. [ABSTRACT FROM AUTHOR]

The process of denoising of medical images that are corrupted by noise is considered as a long established setback in the signal or image processing domain. An effective system for denoising in order to remove white, salt and also pepper noises by means of merging the Long Short-Term Memory, otherwise known as LSTM, based Batch Normalization and Recurrent Neural Network or RNN techniques have been proposed in this research paper. The images of the lung CT are considered as an input in this particular work. Following this, an effectual batch size is calculated by employing the method of Particle Swarm Optimization (PSO). To denoise the image, Recurrent Neural Network or RNN algorithm were proposed, here to reduce the internal covariate shift present in the neural networks, the Long Short-Term Memory or LSTM based Batch Normalization is brought-in. With respect to SNR or Peak Signal to Noise Ratio and Mean Square Error (MSE), operations were assessed. This algorithm is considered as competitive to other denoising schemes which have been confirmed by the experimental outcomes. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms have been used in a wide range of applications in clinical aided diagnosis, such as automatic MR image segmentation and seizure EEG signal analyses. In recent years, many machine learning-based automatic MR brain image segmentation methods have been proposed as auxiliary methods of medical image analysis in clinical treatment. Nevertheless, many problems regarding precise medical images, which cannot be effectively utilized to improve partition performance, remain to be solved. Due to the poor contrast in grayscale images, the ambiguity and complexity of MR images, and individual variability, the performance of classic algorithms in medical image segmentation still needs improvement. In this paper, we introduce a distributed multitask fuzzy c-means (MT-FCM) clustering algorithm for MR brain image segmentation that can extract knowledge common among different clustering tasks. The proposed distributed MT-FCM algorithm can effectively exploit information common among different but related MR brain image segmentation tasks and can avoid the negative effects caused by noisy data that exist in some MR images. Experimental results on clinical MR brain images demonstrate that the distributed MT-FCM method demonstrates more desirable performance than the classic signal task method. [ABSTRACT FROM AUTHOR]

Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool based classifier (Weighted Neighbour Distance using Compound Heirarchy of Algorithms Representing Morphology) and VGG-19 deep convolutional neural network (DNN). For experimentation, DICOM images are collected from reputed government hospital and the proposed intelligent system categorized the tumor into four grades such as low grade glioma, oligodendroglioma, anaplastic glioma and glioblastoma multiform. After preprocessing, features are extracted, optimized and then classified using Windchrm tool where the most significant features are selected on the basis of Fisher score. In the case of DNN classifier, data augmentation is also performed before applying the images into the deep learning network. The performance of the classifiers are analysed with various measures such as accuracy, precision, sensitivity, specificity and F1-score. The results showed reasonably good performance with a maximum classification accuracy of 92.86% for the Wndchrm classifier and 98.25% for VGG-19 DNN classifier. The results are also compared with similar recent works and the proposed system is found to have better performance. [ABSTRACT FROM AUTHOR]

In this paper we systematically evaluate the performance of several state-of-the-art local feature detectors and descriptors in the context of longitudinal registration of retinal images. Longitudinal (temporal) registration facilitates to track the changes in the retina that has happened over time. A wide number of local feature detectors and descriptors exist and many of them have already applied for retinal image registration, however, no comparative evaluation has been made so far to analyse their respective performance. In this manuscript we evaluate the performance of the widely known and commonly used detectors such as Harris, SIFT, SURF, BRISK, and bifurcation and cross-over points. As of descriptors SIFT, SURF, ALOHA, BRIEF, BRISK and PIIFD are used. Longitudinal retinal image datasets containing a total of 244 images are used for the experiment. The evaluation reveals some potential findings including more robustness of SURF and SIFT keypoints than the commonly used bifurcation and cross-over points, when detected on the vessels. SIFT keypoints can be detected with a reliability of 59% for without pathology images and 45% for with pathology images. For SURF keypoints these values are respectively 58% and 47%. ALOHA descriptor is best suited to describe SURF keypoints, which ensures an overall matching accuracy, distinguishability of 83%, 93% and 78%, 83% for without pathology and with pathology images respectively. [ABSTRACT FROM AUTHOR]