Sharmin Akter

Sharmin Akter

Chest radiography is climacteric in identifying different pulmonary diseases, yet radiologist workload and inefficiency can lead to misdiagnoses. Automatic, accurate, and efficient segmentation of lung from X-ray images of chest is paramount for early disease detection. This study develops a deep learning framework using a Pix2pix Generative Adversarial Network (GAN) to segment pulmonary abnormalities from CXR images. This framework's image preprocessing and augmentation techniques were properly incorporated with a U-Net-inspired generator-discriminator architecture. Initially, it loaded the CXR images and manual masks from the Montgomery and Shenzhen datasets, after which preprocessing and resizing were performed. A U-Net generator is applied to the processed CXR images that yield segmented masks; then, a Discriminator Network differentiates between the generated and real masks. Montgomery dataset served as the model's training set in the study, and the Shenzhen dataset was used to test its robustness, which was used here for the first time. An adversarial loss and an L1 distance were used to optimize the model in training. All metrics, which assess precision, recall, F1 score, and Dice coefficient, prove the effectiveness of this framework in pulmonary abnormality segmentation. It, therefore, sets the basis for future studies to be performed shortly using diverse datasets that could further confirm its clinical applicability in medical imaging.

Abu Kaisar Mohammad Masum, Sheikh Abujar, Sharmin Akter et al.

An AI chatbot provides an impressive response after learning from the trained dataset. In this decade, most of the research work demonstrates that deep neural models superior to any other model. RNN model regularly used for determining the sequence-related problem like a question and it answers. This approach acquainted with everyone as seq2seq learning. In a seq2seq model mechanism, it has encoder and decoder. The encoder embedded any input sequence, and the decoder embedded output sequence. For reinforcing the seq2seq model performance, attention mechanism added into the encoder and decoder. After that, the transformer model has introduced itself as a high-performance model with multiple attention mechanism for solving the sequence-related dilemma. This model reduces training time compared with RNN based model and also achieved state-of-the-art performance for sequence transduction. In this research, we applied the transformer model for Bengali general knowledge chatbot based on the Bengali general knowledge Question Answer (QA) dataset. It scores 85.0 BLEU on the applied QA data. To check the comparison of the transformer model performance, we trained the seq2seq model with attention on our dataset that scores 23.5 BLEU.