Year: 2025
Type: article
Abstract: Deep learning has become a potent instrument in medical image analysis, showing great promise for raising the precision and effectiveness of breast cancer diagnosis and detection.A succinct summary of the developments and uses of deep learning methods for breast cancer imaging is given in this study.We talk about the application of many deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs),Long Short-Term Memory (LSTM), and Generative Adversarial Networks (GANs), in the analysis of histopathological pictures, ultrasounds, and mammograms.We highlight the capability of deep learning models to automatically learn complex features from medical images, leading to improved detection of subtle cancerous patterns and reduced false positives.Furthermore, we address the challenges and future directions in this field, such as the need for large and diverse datasets, model interpretability, and clinical validation.Overall, deep learning holds great promise for revolutionizing breast cancer screening and diagnosis, ultimately leading to better patient outcomes
Authors: Nikhil Sharma, Saurabh Srivastava, Neeraj Kumari, Aditya Gupta, Aryan Sharma, Himanshu Tyagi
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411294628