Skin Cancer Prediction using Convolutional Neural Network

Skin cancer is a highly death-dealing and life-threatening disease. It mostly appears in the form of melanoma (malignant) and benign tumours; diagnosing these in the early stages necessitates the use of an efficient algorithm that can be predicted employing an enormous data set that has been trained. The goal of this study is to predict skin cancer using Keras classification model and CNN which uses machine learning algorithms to accurately diagnose and predict the type of skin cancer. We are utilising an optimizer defined for expressing the loss and hyper parameters that have a substantial impact on the model's performance. Our Results show that the suggested approach performs better than the other options, with an accuracy of around 92%.Therefore as an outcome, the goal of this paper is to develop an accurate Keras classification model and CNN model with Optimizers to detect skin cancer with greater than 80% accuracy and a false predictively rate of less than 10%, as well as to visualize skin lesion images from the ISIC dataset

A Enhanced Approach for Identification of Tuberculosis for Chest X-Ray Image using Machine Learning

Lungs are the primary organs affected by the infectious illness tuberculosis (TB). Mycobacterium tuberculosis, often known as Mtb, is the bacterium that causes tuberculosis. When a person speaks, spits, coughs, or breathes in, active tuberculosis can quickly spread through the air. Early TB diagnosis takes some time. Early detection of the bacilli allows for straightforward therapy. Chest X-ray images, sputum images, computer-assisted identification, feature selection, neural networks, and active contour technologies are used to diagnose human tuberculosis. Even when several approaches are used in conjunction, a more accurate early TB diagnosis can still be made. Worldwide, this leads to a large number of fatalities. An efficient technology known as the Deep Learning approach is used to diagnose tuberculosis microorganisms. Because this technology outperforms the present methods for early TB diagnosis, Despite the fact that death cannot be prevented, it is possible to lessen its effects