Detection of Lung Nodule using Novel Deep Learning Algorithm based on Computed Tomographic Images

An improvement of medical field requires a wide range of support from Artificial Intelligence (AI) system and several learning mechanisms. In such case a logic of Medical Image Processing (MIP) oriented concepts are providing a huge support to such medical fields to analyze complex cases in easier manner like tumor, cancer and so on. The major complication of people now-a-days is a Lung Nodule affection and it produce a drastic effect in human life as well as many of the people are affected severely without identifying this disease in earlier stages. So that in this paper a new logic is introduced called as Novel Deep Learning Algorithm (NDLA), in which it provides a marvelous support to physicians to analyze the lung nodules easily and provide the exact scenario of the affection in detail. The proposed NDLA algorithm works based on processing the Computed Tomography (CT) images, in which the process is undergoing into several stages such as Pre-Processing, Lung Region Segmentation and Classification. The logic of Deep Learning initially trains all the input dataset lung images in detail based on the mentioned processes like preprocessing and so on. The processed images are maintained into the repository for testing the real-world patient records. Finally the input patient CT images of the lung is analyzed according to the processed dataset images and extracts the exact scenario of the disease as well as report the details in clear way to the respective individual or physician with proper accuracy details. A novel dataset acquired from Kaggle is used in this scenario to produce the best outcome in results and the resulting section of this paper provides the details in clear manner. For all the proposed Novel Deep Learning Algorithm provides a better solution to analyze the lung nodule disease in dense manner and provide a strong support to medical field for analyzing the complications in earlier stages to save lives of many people.