Lung Nodules Detection from Computed Tomography Scans Using Deep Belief Networks

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Addis Ababa University


Lung cancer is the leading cause of cancer related deaths globally. Analyzing thousands of computed tomography (CT) scans are an enormous burden for radiologists which results for inefficient diagnosis. Hence, a need to read, detect, and provide an evaluations of CT scans efficiently exist to assist radiologists by improving accuracy, time delay to diagnose, human errors, and making bias for specific reasons. Many researches have been conducted to detect lung nodules from CT scans. However, lung nodule detection focusing on the lung than the CT image as a whole has not been conducted so far. Thus, in this research work, lung nodules detection system, which segments lung and lesions from CT images to reduce false positives and employing Deep Belief Network (DBN), is proposed to improve nodule detection accuracy. The study comprises three main phases namely: Image Processing, DBN training and nodules classification. The process starts with DICOM to JPEG conversion. Median filter and histogram equalization are applied for noise removal and contrast adjustment. We designed lung segmentation algorithm from the concept of inverse and intersection operation to separate lung object from the whole CT image. We applied adaptive thresholding for segmenting detail elements of CT images. Lesions on the lung are segmented. This thesis is conducted using datasets, publicly available on Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI). We implemented our methods based on 201 DICOM files that consist of 36,520 samples from which a total of 221,807 lesions are used to prepare feature vectors. This feature vector sets are used as input to the DBN algorithm for training and model construction. A DBN with 5776-500-500-2000-2 architecture, learning rate of 0.1, 100 number of epochs, and backpropagation as fining tuning algorithm is used in constructing the classifier. The DBN classifier was validated with ten-fold cross validation technique. The proposed classifier achieves a sensitivity of 81.143%, specificity of 92.47% and an accuracy of 91.247%. The classifier has also an Area under the ROC curve (AUC) value of 0.882 and 0.924 for malignant and benign cases respectively. Therefore, based on the result we found that DBN model has the potential for lung nodule detection.



Computed Tomography, Ten-fold Cross Validation, DBN, DICOM, AUC, ROC curve