Title: Integrating CNNs and ANNs: a comprehensive AI framework for enhanced breast cancer detection and diagnosis

Authors: Emir Oncu

Addresses: Faculty of Engineering, Department of Computer Engineering, Fatih Sultan Mehmet Vakıf University, Merkezefendi Neighborhood, Mevlevihane Street No: 25, Zeytinburnu, Istanbul, 34025, Turkey

Abstract: Among women globally, breast cancer is a major cause of cancer-related death. Accurate and timely diagnosis is essential, and results can be significantly improved. A new era in image analysis has been brought about by the emergence of artificial intelligence (AI), which has made significant progress in the diagnosis and customisation of treatment plans for breast cancer possible. This study aimed to develop a comprehensive AI framework for breast cancer detection by integrating convolutional neural networks (CNNs) for image analysis with an artificial neural networks (ANNs) for clinical data. Using a dataset of ultrasound and pathology images, along with clinical features from 569 patients, we trained CNN models to classify breast tissue as benign or malignant, and the ANN to process clinical data for the same task. The results demonstrate that the fusion of CNNs and ANNs enhances diagnostic accuracy and offers a promising tool for early breast cancer detection.

Keywords: breast cancer; convolutional neural network; CNN; imaging; machine learning; prediction.

DOI: 10.1504/IJCAST.2025.145833

International Journal of Complexity in Applied Science and Technology, 2025 Vol.1 No.3, pp.281 - 299

Received: 30 Oct 2024
Accepted: 25 Nov 2024
Published online: 29 Apr 2025 *

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