Abstract: Convolutional neural networks, or CNNs, are now the backbone of medical image processing and have revolutionized the interpretation and application of different medical data in clinical image, video,- decision-making in different classifications With a focus on significant advancements, cutting-edge trends, and enduring difficulties in the area, this survey study describes the investigation of CNN-based architectures for medical image processing.
The study began with basic models of CNN, such as LeNet, AlexNet, VGG, and ResNet, before heading to the advanced architectures used in DenseNet, U-Net, and Vision Transformers (ViTs). From these architectures, the discussion reflects their applications to medical image tasks such as disease classification, organ and lesion segmentation, and anomaly detection that cut across imaging modalities like Pathology, Colonoscopy MRI, CT scans.
The survey article provides a broad overview of Convolutional Neural Networks (CNNs), focusing on their applications in medical imaging. It demonstrates how various forms of CNN architectures are used for the interpretation of different types of medical imaging data such as x-ray, CT, MRI and ultrasound images.
The paper covers the developments in CNN methods and their capability in analyzing complex medical data sets and performing tasks such as disease identification, organ delineation and abnormality recognition. In this regard, the survey gives an explanation of the use of CNNs in medical images, and those features provide possibilities for predicting changes in the course of the disease and improve the results of treatment.
Keywords: Deep -learning, Medical Image, CNN Architectures, Image Classification.
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DOI:
10.17148/IJARCCE.2025.14318
