Lung Cancer may be a variety of Cancer that begins in the Lungs because of those that smokes often. However, there Area unit rare probabilities those area unit non-smokers get Affected because of unhealthy pollution and Harmful gasses. The detection of tumor is incredibly vital that helps to detect affected neoplasm areas in the lungs. Computed tomography help us to understand the cancer positions in patients. The detection of cancer tumours are performed by scanning the images of computed tomography. Lung cancer identification system goes with a method of Morphological opening and Gray level co-occurrence matrix (GLCM) feature extraction and Normalized cross-correlation with patches Analysis. Lung cancer classification using Linear Discriminant Analysis (LDA) gives good results of Accuracy of 81.81%. Patch Analysis is a new method to find lung cancer.

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Breast cancer is the second leading cause of death for women everywhere in the world. Since the reason behind the disease remains unknown, early detection and diagnosis is the key challenge for breast cancer control. In this work, mammogram images are initially subject to pre-processing using Laplacian ilter for enhancement of tumour regions, Gaussian mixture model, Gaussian kernel FCM, Otsu global thresholding and FCM technique are employed for segmentation. Further, the ef iciency of segmentation techniques is analyzed by classifying the samples into benign, malignant and healthy using Gray Level Co-occurrence Matrix (GLCM) features. Linear discriminant analysis classiier is used a combination based on which ef iciency used for classi ication of mammograms. Ensemble methods are evaluated. The ef iciency has resulted in better accuracy with the ensemble-based method. The experimentation is conducted in the mini MIAS database of mammograms, and the ef iciency of the linear discriminant analyzer is found to be 89.19% for GKFCM, 83.78% with Otsu and 78.38% with FCM method with GLCM features.