Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification

2024/10/22 by Ganga Prasad Basyal, Basyal, Ganga Prasad, David Zeng +3
Computer Science · Neuroscience · #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2410.16711

openalex publication_date 2024/10/22 · openalex created_date 2024/11/13 · openalex updated_date 2026/07/28

Abstract

The application of deep learning-based architecture has seen a tremendous rise in recent years. For example, medical image classification using deep learning achieved breakthrough results. Convolutional Neural Networks (CNNs) are implemented predominantly in medical image classification and segmentation. On the other hand, transfer learning has emerged as a prominent supporting tool for enhancing the efficiency and accuracy of deep learning models. This paper investigates the development of CNN architectures using transfer learning techniques in the field of medical image classification using a timeline mapping model for key image classification challenges. Our findings help make an informed decision while selecting the optimum and state-of-the-art CNN architectures.

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