Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification

Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification

Abstract Colorectal cancer (CRC) remains one of the leading causes of cancer-related deaths worldwide, necessitating accurate and efficient diagnostic tools. This study proposes an advanced deep learning-based framework for classifying CRC histological images into eight categories: Adipose, Complex,...

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Bibliographic Details
Main Authors: Pragati Patharia, Prabira Kumar Sethy, K. Lakshmipathi Raju, Anita Khanna, Ashoka Kumar Ratha, Santi Kumari Behera, Aziz Nanthaamornphong
Format: Article
Language:English
Published: Springer 2025-07-01
Series:Discover Artificial Intelligence
Subjects:
Colorectal cancer classification
Darknet53
SVM
Random grid search
Histological image analysis
Online Access:https://doi.org/10.1007/s44163-025-00404-8
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Summary:Abstract Colorectal cancer (CRC) remains one of the leading causes of cancer-related deaths worldwide, necessitating accurate and efficient diagnostic tools. This study proposes an advanced deep learning-based framework for classifying CRC histological images into eight categories: Adipose, Complex, Debris, Empty, Lympho, Mucosa, Stroma, and Tumor. Initially, 18 pre-trained Convolutional Neural Networks (CNNs) were evaluated using transfer learning techniques. Statistical analysis via Duncan's Multirange test identified Darknet53 as the most effective model, achieving an accuracy (Acc.) of 92.43%, sensitivity (Sen.) of 92.43%, specificity (Spec.) of 98.51%, precision (Prec.) of 92.70%, and F1-score of 92.45%. To enhance the classification performance, Darknet53 was hybridized with a SVM by replacing the dense layer, and hyperparameters were optimized using a Random Grid Search algorithm. The optimized hybrid model exhibited a remarkable improvement, with an Acc. of 99.7%, Sen. of 99.7%, Spec. of 99.91%, Prec. of 99.98%, and F1-score of 99.98%, alongside significant improvements in other metrics. This hybrid approach demonstrates substantial potential in automating CRC diagnosis with high Acc., offering a valuable tool for clinical use.
ISSN:2731-0809

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