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This paper proposes an intellectual classification system to classify normal and abnormal CT scan lung images. Prediction of cancer cells from lung image is such a difficult task for physicians and researchers. A novel technique for lung tumour detection with Grey Wolf Optimized & Whale Optimization Algorithm – Support Vector Machine (GWO & WOA-SVM) is proposed. CT scan images are used as input images. Investigations focused on the segmentation and classification part to find the lung lesion region. For finding the lung abnormality and lesion region in the lung, four different stages are used. The first stage is the image acquisition process. The second stage is the image pre-processing and enhancement stage, here Advanced Clustering (AC) technique is used. The next stage is the segmentation process, here the advanced surface normal overlap (ASNO) lung segmentation algorithm is used. Finally, the lung lesion is classified using the hybrid classifier followed by a different optimization technique. The research gap for this paper is to find the lung lesion using an optimization-based hybrid classification technique. Nowadays, classification decision and treatment of lung tumours are based on symptoms and radiological appearance. Here Hybrid Classifier (KNN-SVM) is used to classify 700 images; it is observed from the results that the Hybrid classifier KNN-SVM demonstrated the highest classification accuracy rate of 97.6% among other methods. [ABSTRACT FROM AUTHOR]

Medical image detection and classification play an important role in medical research. Tumour detection is a process of crucial importance in oncology. The aim of this paper is to devise an approach for efficient image enhancement, tumour detection, and classification on computed tomographic lung images. Three different application-related methods on computed tomography (CT) images have been designed and implemented in this work. The first method, which was based on image quality, enhances the performance of the medical images. Generally, the CT images are very sensitive to noise and are difficult to handle. Proper care may be taken by introducing some preprocessing algorithms like enhancement algorithms and filters. According to this, an image enhancement algorithm, Wavelet Shrinkage adaptive histogram Equalization (WSAHE), with anisotropic diffusion filter (ADF) was introduced to improve the contrast of the CT images and denoising. In the second method Seed Region growing with Random walk segmentation algorithm (SRGWRWS) is proposed for lung tumour segmentation. In the third method GLCM features, FOS features, structural features, and combinational features are calculated from the segmented lung images. After segmentation, the tumour classification is done by Multi-class support vector machine (MCSVM) and Descend RUSBOOST classifiers. Here, MCSVM classifier was used to find whether the input CT tumour image is suspicious, benign or malignant. Overall accuracy of 92% is obtained using Descend RUSBOOST classifier with GLCM features, FOS features, structural features, combinational features and overall accuracy of 97% is obtained using MCSVM classifier with GLCM feature in MATLAB 2017a software. [ABSTRACT FROM AUTHOR]

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]