Optimized Hybrid CNN Framework for Enhanced Tumor Classification in Breast Cancer Diagnosis

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...

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Vydáno v:International journal of imaging systems and technology Ročník 35; číslo 6
Hlavní autoři: Batool, Shumaila, Zainab, Saima, Usman, Muhammad, Pu, Juhua
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Wiley Subscription Services, Inc 01.11.2025
Témata:
Abnormalities
Accuracy
Algorithms
Artificial neural networks
Back propagation
Back propagation networks
Breast cancer
Classification
Diagnosis
Effectiveness
Machine learning
Medical diagnosis
Medical imaging
Neural networks
Optimization
Optimization techniques
Pattern recognition
Performance enhancement
Statistical analysis
Tumors
Variance analysis
ISSN:0899-9457, 1098-1098
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Shrnutí: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.
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ISSN:0899-9457
1098-1098
DOI:10.1002/ima.70252