ABSTRACT
Breast cancer is largely responsible for female mortality across the globe. Infrared imaging helps screen early abnormal signs without any invasion based on the difference in contralateral temperatures of the breasts and can be used to improve the patient survival rate. Image data is huge to process as it is. In this work, 15 biostatistical features are extracted from the breast region. Using feature selection to achieve high performance prediction, the designed Multi-Layer Perceptron (MLP) with back propagation mechanism employs 9 significant features to classify the thermograms as malignant or benign. For this research work, thermal images from the public Visual Lab dataset have been used. The best performance evaluation metrics, viz., accuracy, sensitivity and specificity obtained are 93.8%, 90% and 95.5%, respectively for the model with 10 neurons in the hidden layer. The outcome is promising with the value of overall Area Under the Curve (AUC) greater than 0.9 for both classes. The design of MLP with the gradient descent algorithm used in this study outperforms the other neural network models in the literature indicating that a well-designed neural network can boost the capability of thermography to predict breast abnormalities.
