Breast cancer is one of the leading causes of cancer deaths among women in developed countries including India. Mammography is currently the most effective method for detection of breast cancer. Early diagnosis of the breast cancer allows treatment which could lead to high survival rate. This paper presents breast cancer detection in digital mammography using Image Processing Techniques by Artificial Neural Networks. A clinical database of 42 previously verified patient cases are employed and randomly partitioned into two independent sets for training and testing. Gray Level Co-occurrence Matrix (GLCM) features extracted from the known Mammogram images are used to train Artificial Neural Network based detection system. In Testing/Recognition Phase the extracted features of known and unknown Mammogram images are compared for classification of images into malignant and benign. Feed-forward back propagation and Cascadeforward back propagation Artificial Neural Network structures had been trained for detection. The performance is evaluated on the basis of Mean Square Error (MSE) and accuracy of both the structure has been compared.

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Osteoarthritis (OA) of the knee is a common cause of activity restriction and physical impairment in elderly people. Early identification and treatment can help delay the progression of OA. Physicians' visual examination rating is objective, varies across interpretation, and is heavily reliant on their expertise. We use two machine learning approaches (CNN) in this article to automatically estimate the severity of knee OA as described by the Kallgren- Lawrence (KL) grading system. To begin, we use a customized one-stage YOLOv2 network to recognize kneecap based on the size of knee joints scattered in X-ray pictures with poor contrast. Second, we use a new customizable arbitrary loss to fine-tune its most famous Cnn architectures, spanning ResNet, VGG, and DenseNet versions, as well as InceptionV3, to categorize the collected knee joint pictures. To be more explicit, we provide a stronger penalty to misrepresentation with a greater difference between the predicted and actual KL grade, driven by the ordinal character of the knee KL grading assignment. The Osteoarthritis Institute (OAI) collection is used to evaluate the basic X-ray pictures. Under the Jaccard index criterion of 0.75, we acquire a mean Jaccard index of 0.858 and a recall of 92.2 percent for knee joint identification. The fine-tuned VGG-19 model with the provided linear loss achieves the greatest generalization ability of 96.7 percent and mean standard deviation (MAE) of 0.344 on the knee KL grading task. Both knee joint identification and knee KL assessment are at the cutting edge of technology Keywords: Osteoarthritis (OA), Deep Learning, X-rays, CNN

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