Authors : Deepa Sajjanar, B.S. Rekha and G.N. Srinivasan
Abstract: In the field of medicine, identification and treatment of cancer is considered as one of the biggest challenge in treatment of chronic illness. The survival of patients depends on timely detection and cure. Experts use the CT scan or computed tomography scan images of patients to detect and classify nodules, before proceeding with advanced treatment procedures. The present day advances in artificial intelligence, machine learning based on deep learning model can be used to develop sophisticated computer aided diagnosis systems to detect cancerous nodules. The proposed system is based on convolutional neural networks to categorize nodules detected in CT scan images as malignant or benign. Image processing and neural networks have been extensively used in the detection and classification of cancerous nodules. Hence, CNNs are more appropriate, for the task of nodule detection and classification. CNN’s have more properties like multiple feature extraction. When convolution layer, subsampling or pooling layer, fully connected layer such layers are combined, leading to deep CNNs, it helps in increasing the accuracy of classification. The proposed CNN Model will be suitable for the early detection and classification of CT scans images containing nodules with accuracy of 93.52% using the domain knowledge of the CT scan images of lung in the field of medicine and neural network.
Deepa Sajjanar, B.S. Rekha and G.N. Srinivasan, 2019. Lung Cancer Detection and Classification using Convolutional Neural Network. Asian Journal of Information Technology, 18: 233-240.