In today`s digital medicine era, many medical photographs are generated daily. As a result, there is a growing need for sophisticated tools to assist medical professionals across various specialties in their diagnostic efforts. Thanks to the evolution of artificial intelligence, convolutional neural network (CNN) techniques have made significant strides in this field. These algorithms are crucial in medical image categorization, object detection, and semantic segmentation. However, while medical imaging classification has garnered widespread attention, object recognition and semantic imaging segmentation have received less focus. In this review, we will explore the development of object detection and semantic segmentation in medical imaging studies, along with a discussion on how to accurately identify the location and boundaries of diseases.

Object detection, Image classification, Convolution neural network, Deep Learning, Machine Learning

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