Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification
This work addresses the need for efficient architecture selection in medical image classification, but it appears incremental as it builds on existing transfer learning methods without introducing new paradigms.
The paper tackles the problem of selecting optimal CNN architectures for medical image classification by investigating transfer learning techniques, resulting in a timeline mapping model to guide informed decisions.
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.