Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization.

Diabetic retinopathy, a complication of diabetes, damages the retina due to prolonged high blood sugar levels, leading to vision impairment and blindness. Early detection through regular eye exams and proper diabetes management are crucial in preventing vision loss. DR is categorized into five classes based on severity, ranging from no retinopathy to proliferative diabetic retinopathy.

Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.

Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels.

Stacked artificial neural network to predict the mental illness during the COVID-19 pandemic.

The individual's mental health crisis and the COVID-19 pandemic lead to mental disorders. The transmission of the COVID-19 virus is associated with the levels of anxiety, stress, and depression in individuals, similar to other factors. Increases in mental illness cases and the prevalence of depression have peaked after the pandemic struck.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs.

Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM.

Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity.

Automatic Detection and Classification of Diabetic Retinopathy Using the Improved Pooling Function in the Convolution Neural Network.

Diabetic retinopathy (DR) is an eye disease associated with diabetes that can lead to blindness. Early diagnosis is critical to ensure that patients with diabetes are not affected by blindness. Deep learning plays an important role in diagnosing diabetes, reducing the human effort to diagnose and classify diabetic and non-diabetic patients.

Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function.

Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely used in DR detection through the classification of blood vessel pixels from the remaining pixels.

Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN.

Melanoma is easily detectable by visual examination since it occurs on the skin's surface. In melanomas, which are the most severe types of skin cancer, the cells that make melanin are affected. However, the lack of expert opinion increases the processing time and cost of computer-aided skin cancer detection.

Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization.

Diabetic retinopathy (DR) is one of the most important microvascular complications associated with diabetes mellitus. The early signs of DR are microaneurysms, which can lead to complete vision loss. The detection of DR at an early stage can help to avoid non-reversible blindness.