Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/322256
Title: Bio Medical Image Classification for Cancer Detection by Using Feed Forward Back Propagation Neural Network
Researcher: Pankaj Nanglia
Guide(s): Aparna N Mahajan
Keywords: Engineering
Engineering and Technology
Engineering Electrical and Electronic
University: Maharaja Agrasen University
Completed Date: 2020
Abstract: newline Lungs cancer detection using Artificial Intelligence technique is becoming one of the most famous research areas. This is due to increase in the death rate due to lungs cancer. The incidence and humanity rate of lung cancer rank first among various cancers across the world. Once diagnosed, lung cancer often reaches the terminal stage quickly, and thus patients lose the best treatment time. X-ray and CT scanning are often used to screen for early lung cancer patients, but the problems of low sensitivities and specificities exist. Researcher is being done to diagnose cancer with miRNA-21but the sensitivity up to 70-80% is achieved. Soft computing can employ a variety of feature extraction, feature optimization and classification techniques on the biomedical images so as to cure the disease at an early stage. This approach of soft computing is in particular well suited to biomedical applications, those that depend on complex proteomic and genomic measurements. newlineIn current scenario soft computing has been observed in playing an important role especially in the diversified domain of medical sector. Soft Computing is nothing but it is computational intelligence that provides non-invasive techniques to diagnosis the disease without harming the patient. Recent statistics observes that the detection and classification of lung cancer disease is one of the most tiresome tasks in the field of medical area. In the diversified segment of medical industry technology usage plays a very important role. The most challenging task is detection and diagnosis of the lung cancer at an early stage with more accuracy. In this Research work an efficient feature extraction and optimization technique has been implemented on 100 - 500 set of CT images. The performance of these techniques have been analysed on the basis of parameter minimum execution time with minimum error rate. The main task of soft computing is to train the data set and classify the data set according to the requirement. newline
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URI: http://hdl.handle.net/10603/322256
Appears in Departments:Maharaja Agrasen Institute of Technology



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