Cancer is becoming the most toxic ailment identified among individuals worldwide. The
mortality rate has been increasing rapidly every year, which causes progression in the …

Background Accurate diagnosis of breast lesions and discrimination of axillary lymph node
(ALN) metastases largely depend on radiologist experience. Purpose To develop a deep …

Background Deep learning (DL) have been reported feasible in breast MRI. However, the
effectiveness of DL method in mpMRI combinations for breast cancer detection has not been …

Aortic stenosis (AS) is a critical cardiovascular condition that necessitates precise diagnosis
for effective patient care. Despite a limited dataset comprising only 202 images, our study …

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper
classification of these abnormalities enables them to take informed actions, leading to early …

This paper presents an effective learning multi-spike deep spiking neural network with
temporal feedback backpropagation for breast cancer detection using contrast-enhanced …

Detection of metastatic breast cancer lesions is a challenging task in breast cancer
treatment. The recent advancements in deep learning gained attention owing to its …

Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been
used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal …

Breast cancer that begins in the milk ducts and spreads to nearby tissue is known as
invasive ductal carcinoma (IDC). Machine learning methods may be used to classify IDC …

The Internet of Medical Things (IoMT) can automate breast tumor detection and classification
with the potential of artificial intelligence. However, the leakage of sensitive data can cause …