An Infrared High classification Accuracy Hand-held Machine Learning based Breast-Cancer Detection System

Breast cancer is the leading type of cancer among women in the 3
^rd
world countries with < 50% survival rate. However, its early diagnosis can lead to cost-effective and successful treatment. Traditional breast cancer screening tools like mammography and MRI are not readily available to the population in low-income countries. Thermography (infrared imaging) is an FDA approved adjunct screening tool which can be an alternative solution. We present here the architecture of thermography based, application-specific Digital Back End (DBE) processor for a handheld off the shelf portable and intelligent screening device. A thermal image of the thorax taken by an infrared camera is pre-processed to get the regions of interest. To achieve efficient hardware implementation texture features are carefully selected, which are then fed to a dual classifier based on trained Linear Support Vector Machine (LSVM) and convolutional neural network (CNN) to decide the decision boundary. The proposed system achieves an overall sensitivity and specificity of 90.06% and 91.8%, respectively, with efficient hardware implementation by exploiting proposed classifier.