It is a tumor that grows in mediastinum region of chest that separates the lungs is termed as Mass.11 Nodule: A small masses of tissue in the lung are known as lung nodules.
12 Pleural Thickening: When the lung is exposed to asbestos, it causes lungs tissue to scar. This condition is known as pleural thickening.
13 Pneumonia: When there is an infection in air sacs of either or both lungs, then its results in Pneumonia.
14 Pneumothorax: When air leaks from lungs into the chest wall then this condition is known as Pneumothorax disorder.
Table 2.1 Details of ChestX-ray14 dataset.
| Type of pathology | No. of images with label | Type of pathology | No. of images with label |
|---|---|---|---|
| Atelectasis | 11559 | Consolidation | 4,667 |
| Cardiomegaly | 2776 | Edema | 2,303 |
| Effusion | 13317 | Emphysema | 2,516 |
| Infiltration | 19894 | Fibrosis | 1,686 |
| Mass | 5782 | Pleural thickening | 3,385 |
| Nodule | 6331 | Hernia | 227 |
| Pneumonia | 1431 | Normal chest x-ray | 60,412 |
| Pneumothorax | 5302 |
Figure 2.2 Types of chest pathologies.
Detection of Cardiomegaly is done by many researchers as it is a spatially spread disorder across large region and therefore easy to detect.
2.3 Existing Models
Models proposed in the past are mainly classified into two types: ensemble models and hybrid and pretrained models. Ensemble models either focused on classifying all fourteen pathologies or limited abnormalities like cardiomegaly, Edema, Pneumonia, or COVID-19. In pretrained models, initialization of parameters of deep learning models is done from ImageNet dataset, and then, the network is fine-tuned as per the pathologies targeted. This section deals with discussion on various existing models implemented in the literature along with issues they have addressed related to x-ray images, datasets used for training, and the type of pathologies detected by the model in chronological order of their implementation.
In [4], the deep learning model named Decaf trained on non-medical ImageNet dataset for detection of pathologies in medical CXR dataset is applied. Image is considered as Bag of Visual Words (BoVW). The model is created using CNN, GIST descriptor, and BoVW for feature extraction on ImageNet dataset and then it was applied for feature extraction from medical images. Once the model is trained, SVM is utilized for pathology classification of CXR and the AUC is obtained in the range of 0.87 to 0.97. The results of feature extraction can be further improved by using fusion of Decafs model such as Decaf5, Decaf6, and GIST is presented by the authors. In [41], pre-trained model GoogleNet is employed to classify chest radiograph report into normal and five chest pathologies namely, pleural effusion, consolidation, pulmonary edema, pneumothorax, and cardiomegaly through natural language processing techniques. The sentences were separated from the report into keywords such as “inclusion” and “exclusion” and report is classified into one of the six classes including normal class.
Considering popularity of deep learning, four different models of AlexNet [34] and GoogleNet [65] are applied for thoracic image analysis wherein two of them are trained from ImageNet and two are trained from scratch. Then, these models are used for detecting TB from CXR radiography images. Parameters of AlexNet-T and GoogleNet-T are initialized from ImageNet, whereas AlexNet-U and GoogleNet-U parameters are trained from scratch. The performance of all four models are compared and it is observed that trained versions are having better accuracy than the untrained versions [35].
In another model, focus was given only on eight pathologies of thoracic diseases [70]. Weakly supervised DCNN is applied for large set of images which might have more than one pathology in same image. The pre-trained model is adopted on ImageNet by excluding fully connected and final classification layer. In place of these layers, a transition layer, a global pooling layer, a prediction layer, and a loss layer are inserted in the end after last convolution layer. Weights are obtained from the pre-trained models except transition, and prediction layers were trained from scratch. These two layers help in finding plausible location of disease. Also, instead of conventional softmax function, three different loss functions are utilized, namely, Hinge loss, Euclidean loss, and Cross Entropy loss due to disproportion of number of images having pathologies and without pathology. Global pooling layer and prediction layer help in generating heatmap to map presence of pathology with maximum probability. Moreover, Cardiomegaly and Pneumothorax have been well recognized using the model based on ResNet50 [21] as compared to other pathologies.
In [28], three different datasets, namely, Indiana, JSRT, and Shenzhen dataset, were utilized for the experimentation of proposed deep model. Indiana dataset consists of 7,284 CXR images of both frontal and lateral region of chest annotated for pathologies Cardiomegaly, Pulmonary Edema, Opacity, and Effusion. JSRT consists of 247 CXR having 154 lung nodule and 94 with no nodule. Shenzhen dataset consists of 662 frontal CXR images with 336 TB cases and remaining normal cases. Features of one of the layers from pre-defined models are extracted and used with binary classifier layer to detect abnormality and features are extracted from second fully connected layer in AlexNet, VGG16, and VGG19 network. It is observed that, dropout benefits shallow networks in terms of accuracy but it hampers the performance of deeper networks. Shallow DCN are generally used for detecting small objects in the image. Ensemble models perform better for spatially spread out abnormalities such as Cardiomegaly and Pulmonary Edema, whereas pointed small features like nodules cannot be easily located through ensemble models.
Subsequently, three branch attention guided CNN (AG-CNN) is proposed based on the two facts. First fact is that though the thoracic pathologies are located in a small region, complete CXR image is given as an input for training which add irrelevant noise in the network. Second fact is that the irregular border arises due to poor alignment of CXR, obstruct the performance of network [19]. ResNet50 and DenseNet121 have been used as backbone for two different version of AG-CNN in which global CNN uses complete image and a mask is created to crop disease specific region from the generated heat map of global CNN. The local CNN is then trained on disease specific part of the image and last pooling layers of both the CNNs are concatenated to fine tune the amalgamated branch. For classifying chest pathologies,
