An image-based gangrene disease classification

Abstract

Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable.

A deep learning content-based image retrieval approach using cloud computing

“…The normalization process is done by considering the values in the vector. For example, if the vector is of size 1×4: [4,6,9,11]. To normalize it we need to calculate the l2-norm for this vector, which is √ 4 2 + 6 2 + 9 2 + 11 2 = 15.93.…”

“…A CNN is composed of several hidden layers that execute mathematical computations on the input given by the previous layer and produce an output that is fed into the next layer. Over the past recent years, CNNs have improved the performance of computer vision systems, including feature extraction [9], image classification [10]- [15], pattern recognition [16] and speech recognition [17].…”

Implementation of the C4.5 algorithm for micro, small, and medium enterprises classification

“…CNN algorithm is used for the classification of gangrene disease through high-resolution graphic images. This disease is very deadly because of the lack of blood supply to the body [2]. A similar algorithm, namely custom CNN, to classify images of female faces and male faces, was proposed by Zaman [3].…”