A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope
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 …
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
Incorporating a novel dual transfer learning approach for medical images
Recently, transfer learning approaches appeared to reduce the need for many classified
medical images. However, these approaches still contain some limitations due to the …
medical images. However, these approaches still contain some limitations due to the …
Lung tumor image segmentation from computer tomography images using MobileNetV2 and transfer learning
Background: Lung cancer is one of the most fatal cancers worldwide, and malignant tumors
are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms …
are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms …
[PDF][PDF] Classification of breast cancer images using new transfer learning techniques
Breast cancer is one of the most common types of cancer among women, which requires
building smart systems to help doctors and early detection of cancer. Deep learning …
building smart systems to help doctors and early detection of cancer. Deep learning …
Enhancing Medical Image Reclamation for Chest Samples using B-Coefficients, DT-CWT and EPS Algorithm
This paper introduces a novel approach for medical image reclamation, specifically focusing
on enhancing chest image resolution. The proposed method introduces the Dual-Tree …
on enhancing chest image resolution. The proposed method introduces the Dual-Tree …
Hybrid classical–quantum transfer learning for cardiomegaly detection in chest x-rays
Cardiovascular diseases are among the major health problems that are likely to benefit from
promising developments in quantum machine learning for medical imaging. The chest X-ray …
promising developments in quantum machine learning for medical imaging. The chest X-ray …
Explainability of deep learning models in medical video analysis: a survey
Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine
and have been performing significantly better in comparison to other traditional machine …
and have been performing significantly better in comparison to other traditional machine …
An edge computing-based factor-aware novel framework for early detection and classification of melanoma disease through a customized VGG16 architecture with …
MF Almufareh - IEEE Access, 2024 - ieeexplore.ieee.org
Melanoma is dangerous skin cancer disease with high malignancy potential, necessitates
advanced detection methods for improved patient outcomes. This study proposes a novel …
advanced detection methods for improved patient outcomes. This study proposes a novel …
Recurrence quantification analysis of rs-fMRI data: A method to detect subtle changes in the TgF344-AD rat model
Background and objective Alzheimer's disease (AD) is one of the leading causes of
dementia, affecting the world's population at a growing rate. The preclinical stage of AD lasts …
dementia, affecting the world's population at a growing rate. The preclinical stage of AD lasts …
[HTML][HTML] An Alzheimer's disease classification model using transfer learning Densenet with embedded healthcare decision support system
Abstract Training a Convolutional Neural Network (CNN) from scratch is time-consuming
and expensive. In this study, we propose implementing the DenseNet architecture for …
and expensive. In this study, we propose implementing the DenseNet architecture for …