Lung cancer is one of the life taking disease and causes more deaths worldwide. Early detection and treatment is necessary to save life. It is very difficult for doctors to interpret and identify diseases using imaging modalities alone. Therefore computer aided diagnosis can assist doctors for the early detection of cancer very accurately. In the proposed work, optimized deformable models and deep learning techniques are applied for the detection and classification of lung cancer. This method involves pre-processing, lung lobe segmentation, lung cancer segmentation, Data augmentation and lung cancer classification. The median filtering is considered for pre-processing and the Bayesian fuzzy clustering is applied for segmenting the lung lobes. The lung cancer segmentation is carried out using Water Cycle Sea Lion Optimization (WSLnO) based deformable model. The data augmentation process is used to augment the size of segmented region in order to perform better classification. The lung cancer classification is done effectively using Shepard Convolutional Neural Network (ShCNN), which is trained by WSLnO algorithm. The proposed WSLnO algorithm is designed by incorporating Water cycle algorithm (WCA) and Sea Lion Optimization (SLnO) algorithm. The performance of the proposed technique is analyzed with various performance metrics and attained the better results in terms of accuracy, sensitivity, specificity and average segmentation accuracy of 0.9303, 0.9123, 0.9133 and 0.9091 respectively.

With technological advancements in medical electronics, and computerization of all standard medical institutions, the amount of image data being produced in these fields has been increasing constantly. Each hospital or institute provides services to thousands of patients per day, and most of the diagnosis tools are images for example x-rays, ultrasound scanners, magnetic resonance imaging etc. Since most hospitals keep records of each patient's case history, it is possible to detect any diseases in a person in its early stages itself, by studying the cases of the previous patients who exhibited similar symptoms, and direct the doctors for further tests. A possible environment for testing the aforementioned procedure is to use images of the fundus of the eye, which are used to detect and monitor the presence or progress of diseases pertaining to the eye. Extraction of features from these images that generally indicate the presence of afflictions, namely exudates, is addressed in this paper. A novel algorithm for the detection of the Optical Disk and the presence of any exudates has been described in this paper. The algorithm has been tried in MATLAB version 2011b. An accuracy of 92% was achieved.

No abstract

No abstract

No abstract