Nagarajan, B.; Chakravarthy, S.; Venkatesan, V.K.; Thyluru Ramakrishna, M.; Khan, S.B.; Basheer, S.; Albalawi, E. A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics2023, 13, 3461.
Nagarajan, B.; Chakravarthy, S.; Venkatesan, V.K.; Thyluru Ramakrishna, M.; Khan, S.B.; Basheer, S.; Albalawi, E. A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics 2023, 13, 3461.
Nagarajan, B.; Chakravarthy, S.; Venkatesan, V.K.; Thyluru Ramakrishna, M.; Khan, S.B.; Basheer, S.; Albalawi, E. A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics2023, 13, 3461.
Nagarajan, B.; Chakravarthy, S.; Venkatesan, V.K.; Thyluru Ramakrishna, M.; Khan, S.B.; Basheer, S.; Albalawi, E. A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics 2023, 13, 3461.
Abstract
Oral Squamous Cell Carcinoma is one among the most common cancer and early detection is the main key to avoid deaths. Automated diagnostic tools that process the histopathological images of a patient to detect abnormal oral lesions will be very much useful for the clinicians. A deep learning framework have been designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed Swarm Intelligence technique called Modified Gorilla Troops Optimizer. Various optimization algorithms are implemented in literature for optimal parameter identification, weights updating and feature selection in deep learning models, but this work focuses on usage of optimization algorithm as intermediate layer that transforms the extracted features into the features that are more suitable for classification. Three datasets totally comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures namely InceptionV2, MobileNetV3, and EfficientNetB3 are investigated as feature extraction layers. Two fully connected Neural Network layers along with batch normalization and dropout are used as classification layers. Among the investigated feature extraction models, MobileNetV3 performs well in all the three datasets with the highest accuracy of 0.89. Usage of the proposed Modified Gorilla Troops Optimizer as an intermediate layer boosts this accuracy to 0.95.
Keywords
oral cancer; histopathologic images; CNN; deep learning framework; swarm
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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