The electroencephalogram (EEG) has emerged over the past few decades as one of the key
tools used by clinicians to detect seizures and other neurological abnormalities of the …

To improve the accuracy of tumor identification, it is necessary to develop a reliable
automated diagnostic method. In order to precisely categorize brain tumors, researchers …

Artificial intelligence-powered deep learning methods are being used to diagnose brain
tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic …

Breast cancer is the second leading cause of mortality among women. Early and accurate
detection plays a crucial role in lowering its mortality rate. Timely detection and classification …

Rapid improvements in ultrasound imaging technology have made it much more useful for
screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound …

The current approach to diagnosing and classifying brain tumors relies on the histological
evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual …

The proposed hybrid CNN-BiLSTM architecture aims to address the challenge of detecting
epileptic seizures systematically from EEG signal analysis. The system consists of several …

In recent years, the realms of computer vision and deep learning have ushered in
transformative changes across various domains. Among these, deep learning stands out for …

Simple Summary Within the rapidly evolving landscape of Machine Learning in the medical
field, this paper focuses on the forefront advancements in neuro-oncological radiology. More …

Diabetic retinopathy (DR) and adult vitelliform macular dystrophy (AVMD) may cause
significant vision impairment or blindness. Prompt diagnosis is essential for patient health …