Search

A chronic disorder caused by abnormal growth of the lung cells in the pulmonary tumour. This study suggests a modern automated approach to improve efficiency and decrease the difficulty of lung tumour diagnosis. The proposed algorithm for lung tumour sensing consists of four phases: pre‐processing, segmentation, extraction, and characteristics classification. The first stage is the image acquisition here input lung image is read and then resized. The second stage is the image pre‐processing here Perona–Malik diffusion with unsharp masking filter is proposed for enhancement purposes. The third stage is the segmentation here the improved histogram–based fast 2D Otsu's thresholding is proposed for lung tumour segmentation purposes. Finally, linear discriminant analysis classifier, support vector machine (SVM) classifier, SVM–sequence minimal optimisation classifier, Naive Bayes classifier, SVM–advance sequence minimal optimisation (SVM–ASMO) classification [proposed] included in the various classifier groups adopted in this report. Overall performance accuracy of 0.962 is obtained using the proposed SVM–ASMO method that helps to diagnose the cancer cells using the feature extraction process, which is done automatically. The specificity, precision, recall, and F1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984,respectively. [ABSTRACT FROM AUTHOR]

Nowadays, lung cancer is the leading cause of cancer death in both men and women. The early detection of potentially cancerous cells is the best way to improve the patient's chances of survival. In the medical field, computed tomography (CT) is the best imaging technique and it is helpful for doctors to accurately find the cancerous cells. The authors propose an automatic approach to analyse and segment the lungs and classify each lung into normal or cancer. Initially, the CT lung image is pre‐processed to remove noise. Then, they combine the histogram analysis with thresholding and morphological operations to segment and extract the lung regions. In feature extraction stage, the radiomic features of each lung image are extracted separately. Then to improve the classification accuracy, some of the optimum features are selected using modified graph clustering‐based whale optimisation algorithm. Finally, the selected features are classified using ensemble classifiers such as support vector machine, K‐nearest neighbour, and random forest. Experimental result demonstrates that the proposed method achieves better performance in terms of sensitivity, specificity, precision, recall, F‐measure, and accuracy when compared with other state‐of‐art approaches. [ABSTRACT FROM AUTHOR]