Research Article
Deep Learning in Medical Imaging: A Case Study on Lung Tissue Classification
@ARTICLE{10.4108/eetpht.10.5549, author={Sandeep Kumar Panda and Janjhyam Venkata Naga Ramesh and Abdus Sobur and Mehadi Hasan Bijoy and Mannava Yesubabu}, title={Deep Learning in Medical Imaging: A Case Study on Lung Tissue Classification}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={3}, keywords={Medical imaging, Lung tissue, DL models, Deep Learning models, Histopathology imagery, Dataset, Lung-related ailments, Cancer diagnosis, Performance evaluation, Precision and recall}, doi={10.4108/eetpht.10.5549} }- Sandeep Kumar Panda
Janjhyam Venkata Naga Ramesh
Abdus Sobur
Mehadi Hasan Bijoy
Mannava Yesubabu
Year: 2024
Deep Learning in Medical Imaging: A Case Study on Lung Tissue Classification
PHAT
EAI
DOI: 10.4108/eetpht.10.5549
Abstract
INTRODUCTION: In the field of medical imaging, accurate categorization of lung tissue is essential for timely diagnosis and management of lung-related conditions, including cancer. Deep Learning (DL) methodologies have revolutionized this domain, promising improved precision and effectiveness in diagnosing ailments based on image analysis. This research delves into the application of DL models for classifying lung tissue, particularly focusing on histopathological imagery. OBJECTIVES: The primary objective of this study is to explore the deployment of DL models for the classification of lung tissue, emphasizing histopathological images. The research aims to assess the performance of various DL models in accurately distinguishing between different classes of lung tissue, including benign tissue, lung adenocarcinoma, and lung squamous cell carcinoma. METHODS: A dataset comprising 9,000 histopathological images of lung tissue was utilized, sourced from HIPAA compliant and validated sources. The dataset underwent augmentation to ensure diversity and robustness. The images were categorized into three distinct classes and balanced before being split into training, validation, and testing sets. Six DL models - DenseNet201, EfficientNetB7, EfficientNetB5, Vgg19, Vgg16, and Alexnet - were trained and evaluated on this dataset. Performance assessment was conducted based on precision, recall, F1-score for each class, and overall accuracy. RESULTS: The results revealed varying performance levels among the DL models, with EfficientNetB5 achieving perfect scores across all metrics. This highlights the capability of DL in improving the accuracy of lung tissue classification, which holds promise for enhancing diagnosis and treatment outcomes in lung-related conditions. CONCLUSION: This research significantly contributes to understanding the effective utilization of DL models in medical imaging, particularly for lung tissue classification. It emphasizes the critical role of a diverse and balanced dataset in developing robust and accurate models. The insights gained from this study lay the groundwork for further exploration into refining DL methodologies for medical imaging applications, with a focus on improving diagnostic accuracy and ultimately, patient outcomes.
Copyright © 2024 S. K. Panda et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
