Enhanced prediction using deep neural network-based image classification

ABSTRACT

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.

KEYWORDS:

• Convolutional neural networks
• image classification
• Stochastic Multinomial Logarithmic image classification
• Brodatz texture image
• Peak Signal-to-Noise Ratio

Additional information

Notes on contributors

K. Ramalakshmi

K. Ramalakshmi is Assistant Professor, Department of Electronics and Communication Engineering, P.S.R. Engineering College, Sivakasi, India. She has completed her post graduate, in the year 2008 from Anna University, India. She has published more than 16 technical papers in reputed Journals and Conferences. She is a life member of ISTE. Her area of interest includes Medical Image Processing, Image Segmentation, Machine Learning.

V. Srinivasa Raghavan

V. Srinivasa Raghavan, Graduated in Electronics and Communication Engineering from Madurai Kamaraj University and obtained his Post Graduate Degree in Electrical Engineering from Anna University, Chennai. He completed his Ph.D. from MS University, Tirunelveli in the area of Image Compression. He has published more than 50 technical papers in reputed Journals and Conferences. His area of interest includes Medical Image Processing, Pattern Recognition, Image Segmentation, etc. He has 34 years of experience in teaching, research and administration in technical institutions. He is currently working as Principal, Theni Kammavar Sangam College of Technology, Theni, Tamilnadu, India.