Optimized Hybrid CNN Framework for Enhanced Tumor Classification in Breast Cancer Diagnosis - Batool, Shumaila; Zainab, Saima; Usman, Muhammad; Pu, Juhua (International journal of imaging systems and technology, 2025)
Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To enable oncologists to diagnose abnormalities promptly, this research proposes an innovative classification framework for breast cancer diagnosis. It integrates an improved optimization method with a hybridized CNN architecture. In this article, a custom CNN, feed‐forward and backpropagation have been implemented. The scaled conjugate algorithm is employed in the feed‐forward paradigm, yielding a formidable accuracy of 99.1%. On the other hand, backpropagation implements stochastic gradient descent and exhibits a remarkable accuracy rate of 97.3%. Additionally, by integrating the grey wolf optimization (GWO) algorithm with the Backpropagation Neural Network (BPNN), model performance is enhanced by optimizing parameters and accuracy to 100%. Furthermore, the custom CNN achieves an incredible 98% accuracy by utilizing the Adam optimizer in conjunction with the ReduceLROnPlateau approach. Statistical analysis utilizing Analysis of Variance (ANOVA) and Honestly Significant Difference (HSD) tests has demonstrated that the suggested hybrid model improves detection accuracy and reliability. These results highlight the adaptability and effectiveness of various optimization techniques in enhancing the performance of neural network models on a range of demanding tasks related to machine learning and pattern recognition.