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Lung Cancer Detection Using Explainable Artificial Intelligence in Medical Diagnosis
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Author(s): M. Sundarrajan (Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, India), Senthil Perumal (Department of ECE, Saveetha School of Engineering, SIMATS University, Chennai, India), S. Sasikala (Department of ECE, Kongu Engineering College, India), Manikandan Ramachandran (School of Computing, SASTRA University (Deemed), Thanjavur, India)and N. Pradeep (Department of Computer Science and Engineering(Data Science), Bapuji Institute of Engineering and Technology, Davangere, India)
Copyright: 2024
Pages: 19
Source title:
Advances in Explainable AI Applications for Smart Cities
Source Author(s)/Editor(s): Mangesh M. Ghonge (Sandip Institute of Technology and Research Centre, India), Nijalingappa Pradeep (Bapuji Institute of Engineering and Technology, India), Noor Zaman Jhanjhi (School of Computer Science, Faculty of Innovation and Technology, Taylor’s University, Malaysia)and Praveen M. Kulkarni (Karnatak Law Society's Institute of Management Education and Research (KLS IMER), Belagavi, India)
DOI: 10.4018/978-1-6684-6361-1.ch013
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Abstract
AI (artificial intelligence), IoT (internet of things), and CC (cloud computing) have all lately gained popularity in the healthcare industry, allowing radiologists to make better decisions. This research proposed a novel technique in lung tumor detection based on cloud-IoT in segmenting and classification using deep learning techniques. Using a cloud-based IoT module, the lung tumour dataset was gathered from multiple healthcare datasets. This data has been segmented using optimized fuzzy C-means neural network (OFCMNN). The pattern segmented area is attained by an optimized version. Then segmented pattern of lung cancer has been classified using ensemble of kernel multilayer deep transfer convolutional learning (KM-DTCL). The presented technique's performance was assessed using a benchmark image lung tumour dataset as well as lung MRI images. When compared to current strategies in the literature, the new method outperformed them in terms of accuracy, recall, precision, AUC, TPR, and FPR. The suggested method outperforms all of the photos in the application dataset in a variety of ways.
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