The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches based on natural language processing and computer vision techniques to create auto medical report generation systems. The auto report generator, producing radiology reports, will significantly reduce the burden on doctors and assist them in writing manual reports. Because the sensitivity of chest X-ray (CXR) findings provided by existing techniques not adequately accurate, producing comprehensive explanations for medical photographs remains a difficult task. A novel approach to address this issue was proposed, based on the continuous integration of convolutional neural networks and long short-term memory for detecting diseases, followed by the attention mechanism for sequence generation based on these diseases. Experimental results obtained by using the Indiana University CXR and MIMIC-CXR datasets showed that the proposed model attained the current state-of-the-art efficiency as opposed to other solutions of the baseline. BLEU-1, BLEU-2, BLEU-3, and BLEU-4 were used as the evaluation metrics.

Interpreting chest x-ray (CXR) to find anomalies in the thoracic region is a tedious job and can consume an ample amount of radiologist's time when there are thousands of them to process. In such scenarios, the Computer-Aided Diagnostic (CAD) systems can help radiologists by doing the trivial processing and presenting the information in a meaningful way so that, the radiologist can make more accurate decisions by spending less amount of time and energy. This research study intends to propose a better, accurate, and efficient CNN based pulmonary disease diagnosis system using CXR images. In the proposed system, the capabilities of deep neural network architecture are exploited by proposing a custom CNN architecture with additional layers and modified hyperparameters to meet the required results. The input CXR is examined for healthy or infected at the surface level and the infected images are further processed for class level label classification. The lung region is segmented from the entire input CXR image to reduce the amount of noise and increase the processing efficiency by processing less overall information. The proposed model is evaluated on the benchmark split of the NIH chest x-ray dataset and achieves better segmentation and classification results when compared to the state of the art approaches.

In this research, a novel customized deep learning model is proposed to detect Tuberculosis (TB) from chest X‐rays (CXR). The model is utilized for three experimentations: (i) classification of CXR image as healthy or TB infected, (ii) sub‐classification of infected images to TB specific manifestations, and (iii) classification of CXR image to thoracic disease manifestations. The National Institute of Health (NIH) CXR is used for experimentation. For the first two experimentations, the subset of the dataset is used containing only 10 TB specific manifestations, whereas, the entire NIH CXR dataset is used for the third experiment. The F1 score for binary classification of TB in experiment 1 is calculated as 0.92 which is higher than the average F1 score of the radiologists. The average accuracy for classifying TB specific manifestations in experiment 2 is recorded as 0.84. Finally, the average accuracy of the thoracic disease classification is recorded as 0.82 in experiment 3. The proposed system outperformed the existing approaches reporting higher AUC for each manifestation. Whereas, to the best of knowledge it is the first such attempt on NIH CXR dataset for TB and TB specific manifestation classification and the proposed system showed promising results.

Pulmonary diseases are very severe health complications in the world that impose a massive worldwide health burden. These diseases comprise of pneumonia, asthma, tuberculosis, Covid-19, cancer, etc. The evidences show that around 65 million people undergo the chronic obstructive pulmonary disease and nearly 3 million people pass away from it each year that make it the third prominent reason of death worldwide. To decrease the burden of lungs diseases timely diagnosis is very essential. Computer-aided diagnostic, are systems that support doctors in the analysis of medical images. This study showcases that Report Generation System has automated the Chest X-Ray interpretation procedure and lessen human effort, consequently helped the people for timely diagnoses of chronic lungs diseases to decrease the death rate. This system provides great relief for people in rural areas where the doctor-to-patient ratio is only 1 doctor per 1300 people. As a result, after utilizing this application, the affected individual can seek further therapy for the ailment they have been diagnosed with. The proposed system is supposed to be used in the distinct architecture of deep learning (Deep Convolution Neural Network), this is fine tuned to CNN-RNN trainable end-to-end architecture. By using the patient-wise official split of the OpenI dataset we have trained a CNN-RNN model with attention. Our model achieved an accuracy of 94%, which is the highest performance.

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