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Brain tumour segmentation is the process of separating the tumour from normal brain tissues. A glioma is a kind of tumour, which fires up in the glial cells of the spine or the brain. This study introduces a technique for classifying the severity levels of glioma tumour using a novel segmentation algorithm, named DeepJoint segmentation and the multi‐classifier. Initially, the brain images are subjected to pre‐processing and the region of interest is extracted. Then, the segmentation of the pre‐processed image is done using the proposed DeepJoint segmentation, which is developed through the iterative procedure of joining the grid segments. After the segmentation, feature extraction is carried out from core and oedema tumours using information‐theoretic measures. Finally, the classification is done by the deep convolutional neural network (DCNN), which is trained by an optimisation algorithm, named fractional Jaya whale optimiser (FJWO). FJWO is developed by integrating the whale optimisation algorithm in fractional Jaya optimiser. The performance of the proposed FJWO–DCNN with the DeepJoint segmentation method is analysed using accuracy, true positive rate, specificity, and sensitivity. The results depicted that the proposed method produces a maximum accuracy of 96%, which indicates its superiority.

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One third of the world's population is thought to have been infected with mycobacterium tuberculosis (TB) with new infection occurring at a rate of about one per second. TB typically attacks the lungs. Indication of cavities in upper lobes of lungs shows the high infection. Traditionally, it has been detected manually by physicians. But the automatic technique proposed in this paper focuses on accurate detection of disease by computed tomography (CT) using computer-aided detection (CAD) system. The various steps of the detection process include the following: (i) image preprocessing, which is done by techniques such as resizing, masking, and Gaussian smoothening, (ii) image egmentation that is implemented by using mean-shift model and gradient vector flow (GVF) model, (iii) feature extraction that can be achieved by Gradient inverse coefficient of variation and circularity measure, and (iv) classification using Bayesian classifier. Experimental results show that its perfection of detecting cavities is very accurate in low false positive rate (FPR).

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