Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Abstract

Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

“…We employed a technique called Gradient-weighted Class Activation Mapping (Grad-CAM) [ 153 ] to graphically demonstrate the reasons why the suggested P(4) model can conclude. By examining the gradient of the classification score concerning the convolutional features that were formed by the network, Grad-CAM can determine which components of an image are most important for classification.…”

“…• Rotation Skip over the value of 0 with the rotation angle vector F Rot . Eq (38) and Eq (39) are applied to perform the rotation on the Z Train portion.…”

“…They were able to obtain a specificity of 88.2% while maintaining a sensitivity of 96.5%. Another study [ 38 ] designed a novel model DCDD_Net used for the classification of several chest diseases by using cough sound images, CT scans, and CXR. They achieved the appropriate accuracy of 98.9% for the classification of chest diseases.…”

“…However, numerous studies [37][38][39][40] used cough sounds for the identification of several chest diseases such as COVID-19, tuberculosis, etc. Kavuran et al [37] conceived of a study that makes use of the DCNN model in conjunction with the continuous wavelet transform (CWT), and scalogram approaches were used for the depiction of COVID-19 anomalies.…”

“…The classification of diseases has been altered as a result of deep learning (DL) models, which have opened up new doors for medical professionals [35][36][37][38][39][40][41][42][43][44]. Chest infections [45], the detection of cancer cells [46], the segmentation and identification of brain and breast tumors [47], and gene analysis [48,49] have been significantly improved as a result of medical systems partnering with convolutional neural networks (CNN).…”