Multistage classification of oral histopathological images using improved residual network

Santisudha Panigrahi; Ruchi Bhuyan; Kundan Kumar; Janmenjoy Nayak; Tripti Swarnkar; Santisudha Panigrahi; Ruchi Bhuyan; Kundan Kumar; Janmenjoy Nayak; Tripti Swarnkar

doi:10.3934/mbe.2022090

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 2: 1909-1925. doi: 10.3934/mbe.2022090

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Multistage classification of oral histopathological images using improved residual network

1.
Department of Computer Science and Engineering, SOA Deemed to be University Bhubaneswar, Odisha-751030, India
2.
Oral Pathology and Microbiology, IMS, SUM Hospital, SOA Deemed to be University Bhubaneswar, Odisha-751030, India
3.
Department of Electronics and Communication Engineering, SOA Deemed to be University Bhubaneswar, Odisha-751030, India
4.
Dept. of CSE, Aditya Institute of Technology and Management, Andhra Pradesh-532201, India
5.
Department of Computer Application, SOA Deemed to be University Bhubaneswar, Odisha-751030, India

Received: 20 October 2021 Accepted: 19 November 2021 Published: 20 December 2021

Oral cancer is a prevalent disease happening in the head and neck region. Due to the high occurrence rate and serious consequences of oral cancer, an accurate diagnosis of malignant oral tumors is a major priority. Thus, early diagnosis is very effective to give the patient a prompt response to treatment. The most efficient way for diagnosing oral cancer is from histopathological imaging, which provides a detailed view of inside cells. Accurate and automatic classification of oral histopathological images remains a difficult task due to the complex nature of cell images, staining methods, and imaging conditions. The use of deep learning in imaging techniques and computational diagnostics can assist doctors and physicians in automatically analysing Oral Squamous Cell Carcinoma biopsy images in a timely and efficient manner. Thus, it reduces the operational workload of the pathologist and enhance patient management. Training deeper neural networks takes considerable time and requires a lot of computing resources, due to the complexity of the network and the gradient diffusion problem. With this motivation and inspired by ResNet's significant successes to handle the gradient diffusion problem, in this study we suggest the novel improved ResNet-based model for the automated multistage classification of oral histopathology images. Three prospective candidate model blocks are presented, analyzed, and the best candidate model is chosen as the optimal one which can efficiently classify the oral lesions into well-differentiated, moderately-differentiated and poorly-differentiated in significantly reduced time, with 97.59% accuracy.
- oral cancer,
- deep learning,
- residual network,
- convolutional neural network,
- histopathology
Citation: Santisudha Panigrahi, Ruchi Bhuyan, Kundan Kumar, Janmenjoy Nayak, Tripti Swarnkar. Multistage classification of oral histopathological images using improved residual network[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 1909-1925. doi: 10.3934/mbe.2022090

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Abstract

Oral cancer is a prevalent disease happening in the head and neck region. Due to the high occurrence rate and serious consequences of oral cancer, an accurate diagnosis of malignant oral tumors is a major priority. Thus, early diagnosis is very effective to give the patient a prompt response to treatment. The most efficient way for diagnosing oral cancer is from histopathological imaging, which provides a detailed view of inside cells. Accurate and automatic classification of oral histopathological images remains a difficult task due to the complex nature of cell images, staining methods, and imaging conditions. The use of deep learning in imaging techniques and computational diagnostics can assist doctors and physicians in automatically analysing Oral Squamous Cell Carcinoma biopsy images in a timely and efficient manner. Thus, it reduces the operational workload of the pathologist and enhance patient management. Training deeper neural networks takes considerable time and requires a lot of computing resources, due to the complexity of the network and the gradient diffusion problem. With this motivation and inspired by ResNet's significant successes to handle the gradient diffusion problem, in this study we suggest the novel improved ResNet-based model for the automated multistage classification of oral histopathology images. Three prospective candidate model blocks are presented, analyzed, and the best candidate model is chosen as the optimal one which can efficiently classify the oral lesions into well-differentiated, moderately-differentiated and poorly-differentiated in significantly reduced time, with 97.59% accuracy.

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