FALSE POSITIVE REDUCTION BASED ON ANATOMICAL CHARACTERIZATION USING DEEP LEARNING NEURAL NETWORK IN LUNG NODULE DETECTION
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 8, Pages 5296-5303
Abstract- Accurate and early diagnosis of Lung Cancer increases survival rate of patient. Diagnosis of Lung Cancer involves identifying tumour as either benign or malignant, this categorizing done by the Image mining techniques called classification. Image classification is the primary domain, in which Deep neural networks play the most important role of medical image analysis. The image classification accepts the given input images and produces output classification for identifying whether the disease is present or not. Image data represents a keystone of many research areas including medicine, forensic criminology, robotics and industrial automation, meteorology and geography as well as education. In this paper proposal methodology is an integration of ensemble classification is completed using the Entropy Weighted Residual Convolution Neural Network (EWRCNN). Finally, the results are evaluated between the samples, compared to FP reduction with Faster R-CNN alone, the inclusion of rule‐based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of the proposed EWRCNN approach to lung nodule detection and FP reduction on CT images. The objective of this research is to predict correct Lung Cancerous nodule and classifying in CT and X-ray image.
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