Abstract
Exact identification of pulmonary nodules with high sensitivity and specificity is basic for programmed lung malignancy analysis from CT scans. In fact, many deep learning-based algorithms gain incredible ground for improving the exactness of nodule recognition; the high false positive rate is yet a difficult issue which restricted the programmed determination. We propose a novel customized Deep Convolutional Neural Network (DCNN) architecture for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks. Moreover, a High Sensitivity and Specificity system is introduced to eliminate the erroneously recognized nodule competitors by following the appearance changes in consistent CT slices of every nodule. The proposed structure is assessed on the open Kaggle Data Science Bowl (KDSB17) challenge dataset. Our strategy can precisely distinguish lung nodules at high sensitivity and specificity and accomplishes 95 % sensitivity.
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Schedule a callMore From: International Journal of Recent Technology and Engineering (IJRTE)
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