Amplification of pixels in medical image data for segmentation via deep learning object-oriented approach

Medical images serve as a very important tool for medical diagnosis. Medical image segmentation is an area of image processing that segments critical parts of a medical image for diagnosis purposes. The emergence of machine learning approach for medical image segmentation specifically by employing Convolutional Neural Network (CNN) has become a ubiquity as other approaches does not able to compete with its robustness and accuracy. However, this approach is very exhaustive in terms of time and computing resources. The CNN approach mostly emphasizes on the spatial information regarding the image without using much of the individual data contained withing the image. Therefore, this paper proposed a method to amplify the pixel data of medical images via Object-oriented Programming (OOP) approach for segmentation using a straightforward sequential deep learning approach. The results indicated that the proposed method allows more than 90 % faster training time with 33.8 seconds average and overall better segmentation performance of 0.744 for balanced-accuracy metric compared to recent state-of-the-art CNN segmentation models such as SegNet and U-Net Models.