Classification Performance Comparisons of Deep Learning Models in Pneumonia Diagnosis Using Chest X-Ray Images

Abstract

In recent years, the analysis of medical images using deep learning techniques has become an area of increasing popularity. Advances in this area have been particularly evident after the discovery of deep artificial neural network models and achieving more successful performance results than other traditional models. In this study, the performance comparison of different deep learning models used to efficiently diagnose pneumonia on chest x-ray images was performed. The data set used in the study consists of a total of 5840 chest x-ray images of individuals. In order to classify these data, three different deep learning models are used: Convolutional Neural Network, Convolutional Neural Network with Data Augmentation and Transfer Learning. The images in the data set were classified into two categories as pneumonia and healthy people using these three deep learning models. The performances of these three deep learning models used in classification were compared in terms of loss and accuracy. In the comparison of three different deep learning models with two different performance values, 5216 chest x-ray images in the data set were used to train the deep learning model and the remaining 624 were used to test the model. At the end of the study, the most successful performance result was obtained by convolutional neural network model applied with data augmentation technique. According to the best results of this study, this model was able to accurately predict the class of 93.4% of the test data.

Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

“…In few existing ML technique, the extraction and evaluation of the BT section is also presented [14]. The ML approaches implemented in earlier research offered a satisfactory classification result on the benchmark as well as the clinical grade MRI slices (94.51%) when a binary classification is employed to categorize the MRI slices into healthy/disease class [10]. The DL supported approaches helped to achieve a better result during the binary as well as the multi-class categorization of the brain MRI slices [7,8].…”

“…In the earlier works, brain MRI segmentation and classification [9] is seperately discussed by the researchers. Further, the existing brain MRI slices are classified using the machine-learning [10] and/or deep-learning [11] methods. The chied motivation of the proposed research is to implement the CNN based joint segmentation and classifcation to enhance the disease detection accuracy for the benchmark as well as the clinical grade images.Further, to improve the classification accuracy, the optimally selected handcrafted features are combined with the deep-features and a binary classifier is implemented to categorize the images.…”

“…The clinical significance of the proposed CADD unit is confirmed by considering the clinically collected real patient's MRI slices of T2 modality. The earlier works implemented with this dataset can be accessed from [9][10][11]. All the real patient's images are collected based on the approved medical protocol and informed consent is obtained from each patient participated in this study and all this information can be found in [10].…”

“…The earlier works implemented with this dataset can be accessed from [9][10][11]. All the real patient's images are collected based on the approved medical protocol and informed consent is obtained from each patient participated in this study and all this information can be found in [10]. The sample clinical images of GBM/LGG can be found in Figure 3 and number of MRI slices considered in this work is available in Table 1.…”

“…The screening of the BT can be performed using signal-assisted-methodologies (recording and examining the electroencephalogram) and image-assisted-procedures (recording the brain section using radiological imaging). The BT detection with imaging practice helps to reach a healthier diagnosis compared to signal supported techniques [8][9][10].…”