Advanced Hybrid Feature Extraction and DeepLab v3+ Semantic Segmentation for Enhanced Breast Cancer Detection with Cuckoo Search-Optimized Neural Network Classifier
DOI:
https://doi.org/10.52783/jns.v14.1619Keywords:
CSA-NN, DTCWT, DWT, EMPO, LBP, PCAAbstract
This paper introduces a hybrid methodology for enhancing breast cancer detection using thermographic images. The approach combines DeepLab v3+ semantic segmentation with advanced feature extraction techniques, improving classification accuracy. DeepLab v3+ isolates regions of interest (ROIs) from thermograms, focusing on areas likely indicating cancerous lesions. Feature extraction methods like morphological statistical features, Discrete Wavelet Transform (DWT), Local Binary Patterns (LBP), and Enhanced Marine Predator Optimization (EMPO)-Optimized Dual-Tree Complex Wavelet Transform (DTCWT) capture diverse thermal patterns indicative of breast cancer. Principal Component Analysis (PCA) is used for dimensionality reduction, enhancing computational efficiency while preserving important information. The extracted features are classified using a Cuckoo Search-Optimized Neural Network (CSA-NN), which optimizes neural network parameters and addresses class imbalance and feature redundancy. Experimental results demonstrate that this hybrid methodology outperforms traditional techniques, achieving high accuracy, sensitivity, specificity, and precision across breast cancer thermography datasets. The proposed method highlights the potential of combining advanced image processing and machine learning for reliable breast cancer detection.
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