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Detection of abnormal regions in brain image is complex process due to its similarity between normal and abnormal regions. This article proposes an automated technique for the detection of meningioma tumor using Gradient Boosting Machine Learning (GBML) classification method. This proposed system consists of preprocessing, feature extraction and classification stages. In this article, Grey Level Co occurrence Matrix (GLCM) features, intensity features, and Gray Level Run Length Matrix features are derived from the test brain MRI image. These derived feature set are classified using GBML classification approach. Morphological functions are used to segment the tumor region in classified abnormal brain image. The performance of the proposed system is evaluated on brain MRI images which are obtained from open access data set. The proposed methodology stated in this article achieves 93.46% of sensitivity, 96.54% of specificity, and 97.75% of accuracy with respect to ground truth images. K E Y W O R D S brain image,

This paper develops a lung nodule detection mechanism using the proposed sine cosine Sail Fish (SCSF) based generative adversarial network (GAN). However, the proposed SCSF-based GAN is designed by integrating the sine cosine algorithm with the SailFish optimizer, respectively. By using pre-processing, lung nodule segmentation, feature extraction, lung cancer detection, and severity level classification methods detection and classification are performed. The pre-processed computed tomography (CT) image is fed to the lung nodule segmentation phase, where the CT image is segmented into different sub-images to exactly detect the abnormal region. The segmented result after segmentation is fed to the feature extraction phase, where the features like mean, variance, entropy and hole entropy, are extracted from the nodule region. The affected regions are accurately detected using the loss function of the discriminator component. Finally, the lung nodules are detected and classified using the proposed SCSF-based GAN. The proposed approach obtained better performance with the accuracy of 96.925%, sensitivity of 96.900% and specificity of 97.920% for the first-level classification, and the accuracy of 94.987%, the sensitivity of 94.962% and specificity of 95.962% for second-level classification, respectively.

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