Prediction of Lungs Cancer in Medical Images Using Deep Learning Approach

Abstract

In this advanced and fast-moving world, imaging techniques are the root of any medical diagnosis system. Encroachment in technology and machine learning methods may well aid radiologists in the verdict of tumors without any aggressive measures. CT images are mostly used to identify the deep defection of human being especially for lungs cancer. In this proposed system, we implemented the automatic prediction of lungs cancer in CT images using a new deep learning architecture named as Improved Cross Channel AlexNet (ICCAN). Segmentation of lungs cancer is performed via super pixel segmentation algorithm. This segmentation is used to remove the null regions in the CT images and also retain the features of tumor to make the system of efficient prediction. Once the image is properly segmented then it was categorized using our proposed deep learning network of ICCAN. Our proposed deep network is designed using transfer learning mechanism from the pretrained model of AlexNet. The simulation results are analyzed and compared the performance of proposed system with two different existing algorithms of HMM and SVM classification in terms of Accuracy. We achieved 99% of accuracy and F1-score of 99.74% which is higher than the previous implementations and also time complexity of our system is very low compared with HMM and SVM.