Preprocessing Techniques for End-To-End Trainable RNN-Based Conversational System

Maziad, Hussein; Rammouz, Julie-Ann; Asmar, Boulos El; Tekli, Joe

doi:10.1007/978-3-030-74296-6_20

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning has been utilized recently to analyze clinical data across several sectors [37,38], and it excels in tasks like image segmentation and recognition [39][40][41]. The author of [2] presents the Deep Neural Network (DNN) model to detect pneumothorax regions in chest X-ray images.…”

Section: Machine and Deep Learning Techniquesmentioning

confidence: 99%

“…Validation accuracy is 0.79% at the 0 th and, after some fluctuation between drops and gains, reaches 0.77% percent accuracy at the 12 th epoch. Filice et al [36] Machine learning annotations NIH chest X-ray dataset Low performance Maziad et al [37] Deep transfer learning Chest X-ray 70.08% Dey [39] Supervised segementation framework Chest X-ray 66.69% Our approach Xception based federated framework Chest X-ray 79.28%…”

Section: Federated Training and Testing Using Clientmentioning

confidence: 99%

See 1 more Smart Citation

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Almadhor¹,

Khan²,

Wechtaisong³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Pneumothorax is a thoracic condition that occurs when a person's lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data. To address this issue, a federated learning framework based on an Xception neural network model is proposed in this research. The pneumothorax medical image dataset is obtained from the Kaggle repository. Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model's performance. Min-max normalization technique is used to normalize the data, and the features are extracted from chest Xray images. Then dataset converts into two windows to make two clients for local model training. Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side. To decrease the over-fitting effect, every client analyses the results three times. Client 1 performed better in round 2 with a 79.0% accuracy, and client 2 performed better in round 2 with a 77.0% accuracy. The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network, reaching a 79.28% accuracy while also providing privacy to the patient's data.

show abstract