Medical Image Classification Based on Deep Features Extracted by Deep Model and Statistic Feature Fusion with Multilayer Perceptron^‬

Abstract

Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Traditional methods rely mainly on the shape, color, and/or texture features as well as their combinations, most of which are problem-specific and have shown to be complementary in medical images, which leads to a system that lacks the ability to make representations of high-level problem domain concepts and that has poor model generalization ability. Recent deep learning methods provide an effective way to construct an end-to-end model that can compute final classification labels with the raw pixels of medical images. However, due to the high resolution of the medical images and the small dataset size, deep learning models suffer from high computational costs and limitations in the model layers and channels. To solve these problems, in this paper, we propose a deep learning model that integrates Coding Network with Multilayer Perceptron (CNMP), which combines high-level features that are extracted from a deep convolutional neural network and some selected traditional features. The construction of the proposed model includes the following steps. First, we train a deep convolutional neural network as a coding network in a supervised manner, and the result is that it can code the raw pixels of medical images into feature vectors that represent high-level concepts for classification. Second, we extract a set of selected traditional features based on background knowledge of medical images. Finally, we design an efficient model that is based on neural networks to fuse the different feature groups obtained in the first and second step. We evaluate the proposed approach on two benchmark medical image datasets: HIS2828 and ISIC2017. We achieve an overall classification accuracy of 90.1% and 90.2%, respectively, which are higher than the current successful methods.

Automatic Detection of a Standard Line for Brain Magnetic Resonance Imaging Using Deep Learning

“…These deep learning technologies have also been applied to medical images. Examples include computed tomography image classification [10,11], feature extraction [12,13] and automatic detection of lung tumors [14,15], and automatic detection of breast tumors on X-ray images [16,17]. These machine-aided diagnostic techniques have supported the efforts of radiologists to achieve more accurate diagnoses and tumor detection.…”

A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks

“…In particular, a network with just three convolution layers seems to be not sufficient for dealing with the complexity of the application at hand. The architecture of Arjmand et al [22] is shown in Figure 7.Lai et al [24] introduced a network with six convolution layers and no fully connected layers. This network achieved the best results, among the competitors of our proposed architecture, for both the optimizers.…”

“…The differences between this network and our network are the size of the kernels used (which is higher than our proposed network), the absence of any regularization layers, the absence of any fully connected layers, and the number of convolutional layers, (which is higher in our model). The main difference in terms of design choices between the architecture of Lai et al [24] and our architecture is the absence of any regularization layers from their architecture. According to the experiments we performed, this seems to be the main cause for the lower performance of the network proposed by Lai and coauthors.…”

“…According to the experiments we performed, this seems to be the main cause for the lower performance of the network proposed by Lai and coauthors. Figure 4 shows the architecture of Lai et al [24].…”

“…Lai et al [24] introduced a network with six convolution layers and no fully connected layers. This network achieved the best results, among the competitors of our proposed architecture, for both the optimizers.…”