Deep Learning Descriptor Hybridization with Feature Reduction for Accurate Cervical Cancer Colposcopy Image Classification

• URL: http://arxiv.org/abs/2405.01600v1
• Date: Wed, 1 May 2024 06:05:13 GMT
• Title: Deep Learning Descriptor Hybridization with Feature Reduction for Accurate Cervical Cancer Colposcopy Image Classification
• Authors: Saurabh Saini, Kapil Ahuja, Siddartha Chennareddy, Karthik Boddupalli,
• Abstract summary: We propose a novel Computer Aided Diagnosis (CAD) system that combines the strengths of various deep-learning descriptors with appropriate feature normalization. Our approach achieves exceptional performance in the range of 97%-100% for both the normal-abnormal and the type classification.
• Score: 0.9374652839580183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cervical cancer stands as a predominant cause of female mortality, underscoring the need for regular screenings to enable early diagnosis and preemptive treatment of pre-cancerous conditions. The transformation zone in the cervix, where cellular differentiation occurs, plays a critical role in the detection of abnormalities. Colposcopy has emerged as a pivotal tool in cervical cancer prevention since it provides a meticulous examination of cervical abnormalities. However, challenges in visual evaluation necessitate the development of Computer Aided Diagnosis (CAD) systems. We propose a novel CAD system that combines the strengths of various deep-learning descriptors (ResNet50, ResNet101, and ResNet152) with appropriate feature normalization (min-max) as well as feature reduction technique (LDA). The combination of different descriptors ensures that all the features (low-level like edges and colour, high-level like shape and texture) are captured, feature normalization prevents biased learning, and feature reduction avoids overfitting. We do experiments on the IARC dataset provided by WHO. The dataset is initially segmented and balanced. Our approach achieves exceptional performance in the range of 97%-100% for both the normal-abnormal and the type classification. A competitive approach for type classification on the same dataset achieved 81%-91% performance.

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