In particular, the medical domain has seen a significant boost in the use of deep learning techniques for gathering more meaningful insights about various complex data sources ranging from radiology scans to medical records. Several application domains have also benefited enormously due to these recent advances. This was primarily due to the recent enhancements in the computational power of the machines and the development of new learning and optimization methods for neural networks. The advancements in the field of deep learning have demonstrated tremendous success in achieving state-of-the-art results in various problems in the fields of computer vision, natural language processing, information retrieval, to name a few. The primary focus of this paper is the development of an automated visual question answering system for the medical domain. Moreover, these portals augmented with automated intelligent systems such as a visual question answering system can help divert a lot of patient communication traffic from hospitals and doctors, thus reducing their stress. These portals can provide good interfaces to the patients for reliable and trustworthy information directly from doctors or healthcare providers compared to the vast amount of misleading information available online. The push towards digital delivery of medical reports to patients and doctors via CAD enhanced online portals has resulted in better communication of information to the patients. Moreover, CAD systems can also help provide deeper insights into the case, which might not be comprehensible to a naked eye, and thus are very useful for providing a second opinion to the doctor. Computer-Aided Diagnosis (CAD) systems 3 have proven to reduce human-generated medical errors. This burdens each medical practitioner to examine a large number of medical reports, which increases the likelihood of human error due to fatigue 2. Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our modelвЂ™s predicted results.ДЄccording to World Health Organization (WHO) 1, over 45% of the countries across the globe have less than one physician available per 1000 population. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. We tackle two types of answer prediction-categorization and generation. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a вЂsecond opinionвЂ™ on medical cases. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decisionВ maker. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing humanВ errors during the diagnosis. Medical images are difficult to comprehend for a person without expertise.

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