Abstract
Lung cancer exhibits one of the lowest survival rates among various cancer types over a five-year period, necessitating the utmost importance of early detection. Lung cancer can be detected in its early stage widely through the Computed Tomography (CT) scans. In recent years, deep learning models, particularly Convolutional Neural Networks (CNNs), have been integrated into end-to-end methodologies for the accurate perception of lung nodules. This research project presents a novel system constructed using CNN, VGG-16, and MobileNet models to automatically classify between nonmalignant and malignant tumor lesions within the lungs. The proposed CNN model, trained with hyper parameters including batch size of 8, learning rate of 0.01 with 50 epochs, achieved a peak accuracy of 95.15%. Likewise, VGG-16 achieved a high accuracy of 95.88% with same hyper-parameters except the learning rate is changed to 0.1. Furthermore, MobileNet demonstrated exceptional performance with an accuracy of 98.39% for learning rate of 0.001 with same values for other hyper-parameters batch size and epochs. The classification of lung cancer lesions into benign and malignant categories was effectively accomplished using the LIDC (Lung Image Database Consortium), IDRI (Image Database Resource Initiative) data sets. This study underscores the efficiency of deep learning-based approaches in automated lung cancer detection and emphasizes the significance of early diagnosis in improving patient outcomes.
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References
Pfeffer M, Ling S (2022) Evolving optimised convolutional neural networks for lung cancer classification. Signals 3:284–295
Suresh S, Mohan S (2020) ROI-based feature learning for efficient true positive pre-diction using convolutional neural network for lung cancer diagnosis. Neural Comput Appl 32:15989–16009
Samhitha B, Mana S, Jose J, Vignesh R, Deepa D (2020) Prediction of lung cancer using convolutional neural network (CNN). Int J 9
Pandit B, Alsadoon A, Prasad P, Al Aloussi S, Rashid T, Alsadoon O, Jerew O (2023) Deep learning neural network for lung cancer classification: enhanced optimization function. Multimed Tools Appl 82:6605–6624
Saleh A, Chin C, Penshie V, Al-Absi H (2021) Lung cancer medical images classification using hybrid CNN-SVM. Int J Adv Intell Inform 7:151–162
Sajja T, Devarapalli R, Kalluri H (2019) Lung cancer detection based on CT scan images by using deep transfer learning. Traitement Du Signal 36:339–344
Tekade R, Rajeswari K (2018) Lung cancer detection and classification using deep learning. In: Fourth international conference on computing communication control and automation (ICCUBEA). IEEE, pp 1–5
Wang Z, Gao X (2018) Improved lung nodule diagnosis accuracy using lung CT images with uncertain class. Comput Methods Programs Biomed 162:197–209
Nithila E, Kumar S (2017) Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Eng Sci Technol Int J 20(3):1192–1202
Dou A, Heng P (2017) Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 64(7):1558–1567
Monkam P, Qian W (2018) CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images. BioMedical Eng OnLine 17(1)
Winkels M, Cohen T (2019) Pulmonary nodule detection in CT scans with equivariant CNNs. Med Image Anal 55:15–26
Bushara AR (2022) Deep learning-based lung cancer classification of CT images using augmented convolutional neural networks. Electron Lett Comput Vis Image Anal 21(1):130–142
Nasser IM, Abu-Naser SS (2022) Lung cancer detection using artificial neural network. Int J Eng Inf Syst 3(3):17–23
Qin R, Wang Z, Jiang L, Qiao K, Hai J, Chen J, Xu J, Shi D, Yan B (2020) Fine-grained lung cancer classification from PET and CT images based on multidimensional attention mechanism. Complexity 2020:1–12
Asuntha A, Srinivasan A (2020) Deep learning for lung cancer detection and classification. Multimed Tools Appl 79:7731–7762
Kasinathan G, Jayakumar S (2022) Cloud-based lung tumor detection and stage classification using deep learning techniques. BioMed Res Int 79:17 p.
Thakur SK, Choudhary J (2020) Lung cancer identification: a review on detection and classification. Cancer Metastasis Rev 39:989–998
Alakwaa W, Badr A (2017) Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). Int J Adv Comput Sci Appl 8(8)
Lakshmanaprabu SK, Ramirez G (2019) Optimal deep learning model for classification of lung cancer on CT images. Futur Gener Comput Syst 92:374–382
Nanglia P, Rathee D (2021) A hybrid algorithm for lung cancer classification using SVM and neural networks. ICT Express 3:335–341
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Hareesh, P.M., Bellamkonda, S. (2023). Deep Learning-Based Classification of Lung Cancer Lesions in CT Scans: Comparative Analysis of CNN, VGG-16, and MobileNet Models. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_25
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DOI: https://doi.org/10.1007/978-981-99-7093-3_25
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