Enhanced prediction using deep neural network-based image classification

The need for deep convolutional neural network is increasing for medical image classification because it provides good performance. This work elucidates the significance of convolutional neural network in making effective detection of clinical diseases by categorizing the clinical images in an organized manner. Clinical diseases are difficult to predict and interpret. To predict diseases from medical images, the Stochastic Multinomial Logarithmic (SML) based image classification method is proposed. To effectively eliminate noise from images, edge-boosting locally adapted space-variant filters are first applied to the texture and medical MRI, and CT images. The SML approach is used to improve feature classification and disease prediction. Accuracy, Peak Signal-to-Noise Ratio (PSNR), precision, recall and specificity performances of the proposed approach are compared with surviving methods. The proposed method produces enhanced performance compared to the existing ones with improved accuracies of 95.8% and 96.2 % respectively, for Brodatz texture and brain MRI, CT images.