Dataset Information

Attention based automated radiology report generation using CNN and LSTM.

ABSTRACT: 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.

SUBMITTER: Sirshar M

PROVIDER: S-EPMC8736265 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Attention based automated radiology report generation using CNN and LSTM.

Sirshar Mehreen M Paracha Muhammad Faheem Khalil MFK Akram Muhammad Usman MU Alghamdi Norah Saleh NS Zaidi Syeda Zainab Yousuf SZY Fatima Tatheer T

PloS one 20220106 1

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 m ...[more]

PMID: 34990477

Similar Datasets

Project description:The prediction of energy consumption in households is essential due to the reliance on electrical appliances for daily activities. Accurate assessment of energy demand is crucial for effective energy generation, preventing overloads and optimizing energy storage. Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques. To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. Followed by that, Modified Deep CNN-Bi-LSTM (Convolutional Neural Network and Bi-directional Long Short Term Memory) with attention mechanism is utilized for regression which extracts temporal and spatial complex features. Deep CNN extracts features impacting energy consumption whereas Bi-LSTM with attention layer finds suitability for regression as it is capable of modelling irregular trends in the time-series components, where the attention mechanism is implemented to enhance the decoder's ability to selectively focus on the most relevant segments of the input sequence. This is achieved through a weighted integration of all encoded input trajectories, allowing the model to dynamically emphasize the vectors that carry the highest significance for accurate predictions. Based on regression outcomes from analysis taken in hourly, daily and monthly time intervals, enhanced prediction accuracy is estimated through evaluation metrics such as MSE (Mean Square Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) which determines the efficacy of the system, where Specifically, the proposed model achieves MSE of 0.123, MAE of 0.22, and MAPE of 324.12. Furthermore, this model demonstrates a training time of 692.12 s and a prediction time of just 1.87 s. Therefore, present research highlights the critical need for accurate energy consumption prediction in households, driven by the increasing reliance on electrical appliances in daily life.

Project description:Background Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeutic responses. Recent deep learning methods for medical image analysis suggest the utility of automated radiologic imaging classification for relating disease characteristics or diagnosis and patient stratification. Methods To develop a hybrid model using the convolutional neural network (CNN) and the long short-term memory recurrent neural network (LSTM RNN) to classify four benign and four malignant breast cancer subtypes. The proposed CNN-LSTM leveraging on ImageNet uses a transfer learning approach in classifying and predicting four subtypes of each. The proposed model was evaluated on the BreakHis dataset comprises 2480 benign and 5429 malignant cancer images acquired at magnifications of 40×, 100×, 200× and 400×. Results The proposed hybrid CNN-LSTM model was compared with the existing CNN models used for breast histopathological image classification such as VGG-16, ResNet50, and Inception models. All the models were built using three different optimizers such as adaptive moment estimator (Adam), root mean square propagation (RMSProp), and stochastic gradient descent (SGD) optimizers by varying numbers of epochs. From the results, we noticed that the Adam optimizer was the best optimizer with maximum accuracy and minimum model loss for both the training and validation sets. The proposed hybrid CNN-LSTM model showed the highest overall accuracy of 99% for binary classification of benign and malignant cancer, and, whereas, 92.5% for multi-class classifier of benign and malignant cancer subtypes, respectively. Conclusion To conclude, the proposed transfer learning approach outperformed the state-of-the-art machine and deep learning models in classifying benign and malignant cancer subtypes. The proposed method is feasible in classification of other cancers as well as diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-023-00964-0.

Dataset Information

Attention based automated radiology report generation using CNN and LSTM.

Publications

Attention based automated radiology report generation using CNN and LSTM.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets