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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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00404-8 |
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| author | Pragati Patharia Prabira Kumar Sethy K. Lakshmipathi Raju Anita Khanna Ashoka Kumar Ratha Santi Kumari Behera Aziz Nanthaamornphong |
| author_facet | Pragati Patharia Prabira Kumar Sethy K. Lakshmipathi Raju Anita Khanna Ashoka Kumar Ratha Santi Kumari Behera Aziz Nanthaamornphong |
| author_sort | Pragati Patharia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-98f7db8208fb4d0ca919c650ad1de7e6 |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-98f7db8208fb4d0ca919c650ad1de7e62025-08-20T04:02:54ZengSpringerDiscover Artificial Intelligence2731-08092025-07-015111510.1007/s44163-025-00404-8Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classificationPragati Patharia0Prabira Kumar Sethy1K. Lakshmipathi Raju2Anita Khanna3Ashoka Kumar Ratha4Santi Kumari Behera5Aziz Nanthaamornphong6Department of ECE, Guru Ghasidas VishwavidyalayaDepartment of ElectronicsDepartment of IT, S.R.K.R Engineering CollegeDepartment of ECE, Guru Ghasidas VishwavidyalayaDepartment of ElectronicsDepartment of Computer Science and Engineering, VSSUTCollege of Computing, Prince of Songkla University, Phuket CampusAbstract 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.https://doi.org/10.1007/s44163-025-00404-8Colorectal cancer classificationDarknet53SVMRandom grid searchHistological image analysis |
| spellingShingle | Pragati Patharia Prabira Kumar Sethy K. Lakshmipathi Raju Anita Khanna Ashoka Kumar Ratha Santi Kumari Behera Aziz Nanthaamornphong Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification Discover Artificial Intelligence Colorectal cancer classification Darknet53 SVM Random grid search Histological image analysis |
| title | Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification |
| title_full | Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification |
| title_fullStr | Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification |
| title_full_unstemmed | Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification |
| title_short | Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification |
| title_sort | hybrid darknet53 svm model with random grid search optimization for enhanced colorectal cancer histological image classification |
| topic | Colorectal cancer classification Darknet53 SVM Random grid search Histological image analysis |
| url | https://doi.org/10.1007/s44163-025-00404-8 |
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