Abstract
Lung nodules are important markers of lung cancer, and prompt treatment makes early discovery much more likely to improve patient survival. Computer-Aided Diagnosis (CAD) systems were developed because radiologists find it difficult and time-consuming to classify malignant nodules in Computed Tomography (CT) images. Deep learning developments have consistently enhanced CAD's ability to screen for lung cancer. To improve the accuracy of pulmonary nodule classification, we use a Transferable Texture-Based Convolutional Neural Network (CNN) in this work. In order to maximize feature representation, our model integrates an Energy Layer (EL) to extract texture-based features from the convolutional layer. To guarantee strong classification performance, the suggested method is assessed using important performance metrics as accuracy, sensitivity, specificity, F1-score, and AUC-ROC.
References
- T.Venkata Krishnamoorthy, C. Venkataiah, Y. Mallikarjuna Rao, D. Rajendra Prasad, Kurra Upendra Chowdary, Manjula Jayamma, R. Sireesha, “A novel NASNet model with LIME explanability for lung disease classification”, Biomedical Signal Processing and Control, Volume 93,2024,106114,
- Shagun Sharma, Kalpna Guleria, “A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks”,Procedia Computer Science, Volume 218,2023, Pages 357-366,
- Yadav, S.S., Jadhav, S.M. Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data 6, 113 (2019)
- [ Lung nodule detection and classification using texture features and SVM." Journal of Medical Imaging, 6(1), 045002
- Sreelatha, G., Govindkar, A., Ushaswini, S. (2023). Modified Cloud-Based Malware Identification Technique Using Machine Learning Approach. In: Rao, B.N.K., Balasubramanian, R., Wang, SJ., Nayak, R. (eds) Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 315. Springer, Singapore. https://doi.org/10.1007/978-981-19-4162-7_17
- Kumar, Rishabh, Anwar, Saeed, and Rahman, Shahinur"Texture-based CNN for lung nodule classification in CT images." IEEE Transactions on Medical Imaging, 2017. 36(4), 991-1000.
- Shen, Dinggang, Zhou, Lin, Yang, Yuan, and Wang, Ge "Hybrid texture-based CNN for lung nodule detection." IEEE Transactions on Biomedical Engineering, 2017. 64(11), 2663-2672.
- Basu, Saurabh, Shah, Rahul, and Hegde, Akshata"Lung nodule classification using local binary patterns and CNNs." Computer Methods and Programs in Biomedicine, 2019, 176, 171-180.
- Zhou, Yifan, Li, Min, and Cai, Weidong, "Multi-scale texture extraction for lung nodule classification." Neurocomputing, 2018, 275, 1914-1923.
- Xie, Lei, Wang, Zhe, and Li, Fei, "Deep learning for lung nodule classification with texture information." Journal of Digital Imaging, 2018, 31(6), 715-726.
- Li, Qiang, Zhang, Yu, and Ding, Xuefeng, "A multi-modal deep learning framework for lung nodule classification." Medical Image Analysis, 2020, 62, 101625
- Liu, Xuan, Wang, Jianzhuang, and Zhang, Xiang, "Hybrid deep learning approach for lung nodule classification." Pattern Recognition,2021. Vol. 112, 107794.
- Tan, Min, Dong, Huimin, and Zhou, Yun, "Fine-tuning texture-based CNNs for lung nodule classification in clinical practice." Journal of Clinical Imaging Science,2022. Vol.12, 45.
- M Bharathi, D Prasad, T Venkatakrishnamoorthy, M. Dharani, “Diabetes diagnostic method based on tongue image classification using machine learning algorithms”, Journal of Pharmaceutical Negative Results,2022, Vol.13(4), pp: 1247-1250.
- K. Ramana, R. M. Mohana, C. K. Kumar Reddy, G. Srivastava and T. R. Gadekallu, "A Blockchain-Based Data-Sharing Framework for Cloud Based Internet of Things Systems with Efficient Smart Contracts," 2023 IEEE International Conference on Communications Workshops (ICC Workshops), Rome, Italy, 2023, pp. 452-457, doi: 10.1109/ICCWorkshops57953.2023.10283747.
