The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …

This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …

Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …

Retinal vessel segmentation is an important task in medical image analysis and has a
variety of applications in the diagnosis and treatment of retinal diseases. In this paper, we …

The use of computer-assisted analysis to improve image interpretation has been a long-
standing challenge in the medical imaging industry. In terms of image comprehension …

Diabetes mellitus causes diabetic retinopathy (DR), which is the primary source of blindness
worldwide. Initial identification and cure are required to postpone or avert visual degradation …

In vitro models are essential to a broad range of biomedical research, such as pathological
studies, drug development, and personalized medicine. As a potentially transformative …

Lately, medical service area has developed quickly with its own advantages and
disadvantages. In this computerized time, giving exact determination in the carefully …

Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to
immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using …

Medical image classification is a very fundamental and crucial task in the field of computer
vision. These years, CNN-based and Transformer-based models are widely used in …