Auto diagnostic system for detecting solitary and juxtapleural pulmonary nodules in computed tomography images using machine learning

Author:

Karrar Ayat,Mabrouk Mai S.,Abdel Wahed Manal,Sayed Ahmed Y.ORCID

Abstract

AbstractLung cancer is one of the most serious cancers in the world with the minimum survival rate after the diagnosis as it appears in Computed Tomography scans. Lung nodules may be isolated from (solitary) or attached to (juxtapleural) other structures such as blood vessels or the pleura. Diagnosis of lung nodules according to their location increases the survival rate as it achieves diagnostic and therapeutic quality assurance. In this paper, a Computer Aided Diagnosis (CADx) system is proposed to classify solitary nodules and juxtapleural nodules inside the lungs. Two main auto-diagnostic schemes of supervised learning for lung nodules classification are achieved. In the first scheme, (bounding box + Maximum intensity projection) and (Thresholding + K-means clustering) segmentation approaches are proposed then first- and second-order features are extracted. Fisher score ranking is also used in the first scheme as a feature selection method. The higher five, ten, and fifteen ranks of the feature set are selected. In the first scheme, Support Vector Machine (SVM) classifier is used. In the second scheme, the same segmentation approaches are used with Deep Convolutional neural networks (DCNN) which is a successful tool for deep learning classification. Because of the limited data sample and imbalanced data, tenfold cross-validation and random oversampling are used for the two schemes. For diagnosis of the solitary nodule, the first scheme with SVM achieved the highest accuracy and sensitivity 91.4% and 89.3%, respectively, with radial basis function and applying the (Thresholding + Kmeans clustering) segmentation approach and the higher 15 ranks of the feature set. In the second scheme, DCNN achieved the highest accuracy and sensitivity 96% and 95%, respectively, to detect the solitary nodule when applying the bounding box and maximum intensity projection segmentation approach. Receiver operating characteristic curve is used to evaluate the classifier’s performance. The max. AUC = 90.3% is achieved with DCNN classifier for detecting solitary nodules. This CAD system acts as a second opinion for the radiologist to help in the early diagnosis of lung cancer. The accuracy, sensitivity, and specificity of scheme I (SVM) and scheme II (DCNN) showed promising results in comparison to other published studies.

Funder

Helwan University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference54 articles.

1. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics, 2021. CA Cancer J Clin 71:7–3

2. Minna JD, Schiller JH (2008) Harrison’s principles of internal medicine, 17th edn. McGraw-Hill, New York, pp 551–562

3. Peña DM, Luo S, Abdelgader AMS (2016) Auto diagnostics of lung nodules using minimal characteristics extraction technique. Diagnostics 6:13

4. Song QZ, Zhao L, Luo XK, Dou XC (2017) Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng 217:8314740

5. Liu Y, Yang J, Zhao D, Liu J (2009) Computer aided detection of lung nodules based on voxel analysis utilizing support vector machines. In: International conference on future biomedical information engineering

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