Browsing by Author "Azade, A."

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A Comprehensive Review of Brain Tumor Detection and Segmentation Techniques
(Springer Science and Business Media Deutschland GmbH, 2023) Azade, A.; Kumar, P.; Kamath Sâ€¤, S.
Brain tumors are particularly dangerous type of tumor, and if this is not treated in time it maybe prove to be deadly and may also spread across other body parts. Brain tumor is the swelling or growth of unwanted tissues in the brain that results from the unregulated and disordered division of cells. The presence of these tissues resulting abnormal behavior and lot of other complications. The detection of brain tumor is done by using different techniques out of which through magnetic resonance images (MRIs). The scanning process is a time-consuming manual task that needs the involvement of medical professionals. Automating the task of detection of the brain tumor while also grading the severity accurately can help in managing the patientsâ€™ disease effectively. As tumor tissue of different patients is different, automating such processes is often a challenging task. Researchers have incorporated image segmentation for extraction of suspicious regions from MRI, using image processing and AI-based techniques. Radiomic analysis also plays a big role in feature extraction processes. In this paper, we present a comprehensive review of existing approaches for brain tumour detection, covering deep neural models, radiomic analysis and segmentation-based methods for brain tumor classification and segmentation, along with a discussion on prevalent issues, challenges, and future directions of research. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Impact of Image Augmentation in COVID-19 Detection Using Chest X-Ray Images
(Institute of Electrical and Electronics Engineers Inc., 2022) Azade, A.; Anand Kumar, M.
COVID-19 continues to have a devastating impact on people's lives worldwide. In order to combat this condition, it is critical to test affected people in a timely and cost-effective manner. Radiological examination is one of the most efficient ways to attain this goal, with the most widely available and least expensive alternative being a CXR. The artificial intelligence and data science communities have aided in the global response to COVID-19, a novel coronavirus. Detection and diagnosis techniques have focused on developing rapid diagnostic approaches based on chest X-rays and deep learning. In this paper, we have analyzed the impact of augmentation in COVID-19 CXR images with normal lung opacity and viral pneumonia images and presented a model for the detection of COVID-19. Â© 2022 IEEE.
Visual Question Answering Using Convolutional and Recurrent Neural Networks
(Springer Science and Business Media Deutschland GmbH, 2023) Azade, A.; Saini, R.; Naik, D.
This paper presents a methodology that deals with the task of generating answers corresponding to the respective questions which are based on the input images in the dataset. The model proposed in this methodology constitutes two major components and then integration of analysis results and features from these components to form a combination in order to predict the answers. We have created a pipeline that first preprocesses the dataset and then encodes the question string and answer string. Using NLP techniques like tokenization and stemming, text data is processed to form a vocabulary set. Yet another experiment with modification in model and approach was performed using easy-VQA dataset which is available publically. This model used the bag of words technique to turn a question into a vector. This approach considered two components separately for text and image feature extraction and merged it to form analysis and generate an answer. Merge is done by using element-wise multiplication. In these approaches, we have used the softmax activation function in the output layer to generate output or answer to the question. When compared to existing methodologies this approach seems comparable and gives decent results. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.