Megha Banerjee's Projects
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Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, so as to assess the characteristics of a breast cancer formation at early stages and classify it as benign or malignant. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%. Paper accepted at IEEE conference.
Heart disease is a major concern. To prevent this, it is important to detect cardiovascular diseases at the early stage. Early discovery of heart infections and constant treatment can lessen the death rate. However, the accurate and effective detection method of heart diseases is necessary to uncover this deadly threat at a very early stage, even without the presence of a medical professional. This paper studies the use of 2Dconvolutional neural network to classify heart sounds into normal and abnormal categories. The paper reports a classification of five designated categories of heart sounds such as artifact, extra heart sound, extra systole, murmur, and normal. For the betterment of the accuracy, we have reduced the number of convolutional neural network layers with a softmax layer at the top. Each convolutional layer is followed by a max pooling and a dropout layer which finally leads to a global average pooling layer. The proposed method achieves an accuracy of 83%.
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