An Enhanced Lung Cancer Identification and Classification Based on Advanced Deep Learning and Convolutional Neural Network

Ammar Hassan, Hamayun Khan, Arshad Ali, Irfan Ud Din, Abdullah Sajid, Mohammad Husain, Muddassar Ali, Amna Naz and Hanfia Fakhar
Additional contact information
Ammar Hassan: 4dots Solutions, 534 Block G1, Johar Town Lahore, 54000, Pakistan
Hamayun Khan: Department of Computer Science, Faculty of Computer Science & IT Superior University Lahore, 54000, Pakistan
Arshad Ali: Faculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia
Irfan Ud Din: Department of Computer Science, Faculty of Computer Science & IT Superior University Lahore, 54000, Pakistan
Abdullah Sajid: 4dots Solutions, 534 Block G1, Johar Town Lahore, 54000, Pakistan
Mohammad Husain: Faculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia
Muddassar Ali: Department of Computer Science & Information Technology Superior University Lahore, 54000, Pakistan
Amna Naz: Department of Computer Science & Information Technology Superior University Lahore, 54000, Pakistan
Hanfia Fakhar: Department of Computer Science & Information Technology Superior University Lahore, 54000, Pakistan

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 2, 136-141

Abstract: In this research, a fast, accurate, and stable system of lung cancer detection based on novel deep learning techniques is proposed. Lung cancer continues to be one of the most monumental global health concerns, which is why there is an urgent need for low-cost and non-invasive screening. Though the diagnostic methods that are most commonly in use include CTscan, X-ray etc. The interpretation by the human eye varies and errors are bound to occur. In response to this challenge, we outline a more automated approach that is based on deep learning models and can be used to classify lung pictures with high levels of accuracy. This research makes use of a large data set of lung scans categorised as normal, malignant, and benign. The first look what the data had in store threw up some correlation with picture size and what seemed to be category differences. Realizing that live feed requires constant input, each picture underwent grayscale conversion and dimensionality reduction. In order to effectively deal with the unbalanced nature of the dataset that was discovered in the study, the Synthetic Minority Oversampling Technique (SMOTE) was applied as a technique. In this presentation, three new designs were introduced: Model I, Model 2, and Model 3. Additionally, one architecture was developed with the purpose of merging the predictions of all three models. Furthermore, out of all the models created, the best model emerged as model 1 with approximately an accuracy of 84%. 7%. But the ensemble strategy which was intended to make the best of each of the models, produced an astounding 82. 5% accuracy. The specific advantages and misclassification behaviors of Model 2 and 3, although less accurate than Model 1 but are currently under evaluation for future Model ensemble improvements. The technique developed using deep learning addresses the challenges at a faster, efficient, and contactless approach to lung cancer analysis. The fact that it is capable of operating in tandem with others diagnostic instruments may help reduce diagnostic errors and enhance patient care. We have addressed this issue so that the various practitioners would be able to read this paper and we can go to the next generation of diagnostic technologies.

Keywords: Computed tomography; Training; Cancer; Cancer detection; Sensitivity; Data Augmentation; Convolutional Neural Network Structure; Convolutional Neural Network (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:2:p:136-141

DOI: 10.61506/01.00308

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