The retina is one of the most metabolically active tissues in the human body. The retinal vessels provide blood to the inner retinal neurons. The retinal blood vessels are affected by diseases such as Hypertensive retinopathy and Diabetic retinopathy. The early diagnosis prevents the patients from blindness andfatality in some cases. Thus, examining the retinal blood vessels becomes an important work of an ophthalmologist. Thus, automated retinal blood vessel segmentation aids the ophthalmologist and makes their work easier. In this paper, a supervised Convolutional Neural Network (CNN) is suggested that enhances the performance of retinal blood vessel segmentation. Three publicly available datasets are used: STARE, DRIVE, and CHASE_DB1. A novel model, ’Staircase-Net,’ is proposed, which has a series of up-sampling and downsampling processes for feature extraction (extracting the thick and thin blood vessel features, respectively). The images in the datasets undergo a series of transformations in the preprocessing steps. The evaluation metricsconsidered are specificity, accuracy, sensitivity, and area under the curve. Finally, the proposed model results are compared with the state-of-the-art techniques.

Computer-aided detection based on Machine Learning (ML) techniques is increasingly used to detect early-stage colorectal polyps from colonoscopy images. This study presents an efficient ML algorithm that analyses colonoscopy images and accurately detects polyps for reliable diagnosis of the early stages ofColo-Rectal Cancer (CRC). The proposed approach consists of mainly image enhancement, which enhances the low illumination colonoscopy images, followed by feature extraction using Discrete Orthonormal Stockwell Transform (DOST) and classification by a Support Vector Machine (SVM) classifier. We present an efficient image enhancement algorithm that highlights the clinically significant features in the colonoscopy image and the DOST feature extraction method to discriminate between the polyp area and non-polyp region in the colonoscopy data. The proposed method has been trained using the publicly available databases CVC ClinicDB and tested using ETIS Larib and CVC ColonDB. A sliding window with NMS-based post-processing is used in theselection of polyps from the test images. The performance measures are found in terms of precision (93.76%), recall (92.71%), F1 score (93.23%) and F2 score (93.54%) for CVC ColonDB database and precision (80.97%), recall (93.12%), F1 score (86.62%) and F2 score (83.13%) for the ETIS Larib database. Comparison with the existing method shows that the proposed approach surpasses the existing one in terms of precision, recall, F1- score, F2-score in the CVC ColonDB, and in terms of recall, F1-score in the Etis Larib database. This method would help doctors with timely evaluation and analysis of anomalies from colonoscopy data, which would help in the early planning of preventive or therapeutic protocols.