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...

Full description

Saved in:
Bibliographic Details
Published in:International journal of imaging systems and technology Vol. 35; no. 6
Main Authors: Batool, Shumaila, Zainab, Saima, Usman, Muhammad, Pu, Juhua
Format: Journal Article
Language:English
Published: New York Wiley Subscription Services, Inc 01.11.2025
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.70252