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Diabetic retinopathy (DR) is a major reason of blindness around the world. The ophthalmologist manually analyzes the morphological alterations in veins of retina, and lesions in fundus images that is a time-taking, costly, and challenging procedure. It can be made easier with the assistance of computer aided diagnostic system (CADs) that are utilized for the diagnosis of DR lesions. Artificial intelligence (AI) based machine/deep learning methods performs vital role to increase the performance of the detection process, especially in the context of analyzing medical fundus images. In this paper, several current approaches of preprocessing, segmentation, feature extraction/selection, and classification are discussed for the detection of DR lesions. This survey paper also includes a detailed description of DR datasets that are accessible by the researcher for the identification of DR lesions. The existing methods limitations and challenges are also addressed, which will assist invoice researchers to start their work in this domain. [ABSTRACT FROM AUTHOR]

The skin is the human body's largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research. [ABSTRACT FROM AUTHOR]

Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and in the mucous membrane, which has protrusions of micro-abnormal tissue that increase the risk of incurable diseases such as cancer. So, the early examination of polyps can decrease the chance of the polyps growing into cancer, such as adenomas, which can change into cancer. Deep learning-based diagnostic systems play a vital role in diagnosing diseases in the early stages. A deep learning method, Graft-U-Net, is proposed to segment polyps using colonoscopy frames. Graft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages. The preprocessing technique is used to improve the contrast of the colonoscopy frames. Graft-U-Net comprises encoder and decoder blocks where the encoder analyzes features, while the decoder performs the features' synthesizing processes. The Graft-U-Net model offers better segmentation results than existing deep learning models. The experiments were conducted using two open-access datasets, Kvasir-SEG and CVC-ClinicDB. The datasets were prepared from the large bowel of the gastrointestinal tract by performing a colonoscopy procedure. The anticipated model outperforms in terms of its mean Dice of 96.61% and mean Intersection over Union (mIoU) of 82.45% with the Kvasir-SEG dataset. Similarly, with the CVC-ClinicDB dataset, the method achieved a mean Dice of 89.95% and an mIoU of 81.38%. [ABSTRACT FROM AUTHOR]