Detectrozen ( Disease Detection )

Vedant Jadhav, Neeraj Chettiar, Saheel Chavan, Smit Mhatre and Bhavna Arora

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 2, 570-581

Abstract: With broad data development in biomedical and healthcare sectors, detailed analyzes of medical data support early detection of illness, patient care and community services. However, the quality of the study is lowered when the content of the medical data is incomplete. Also, various regions exhibit unique features of certain regional diseases. This can hinder disease outbreak forecasting. In this project, we streamline deep learning algorithms to effectively predict chronic disease outbreaks in populations with recurrent diseases. The diagnosis of diseases is a critical and central aspect of medicinal science. Doctors breakdown side effects in the human body more often than not to foresee diseases. In recent times, numerous research strategies have been used with a specific goal to make it more accurate. This system will help to predict the medical results efficiently. In this system, we will provide a user-friendly interface that can be used by the users to detect whether their medical test results are positive or normal, i.e. it will detect the disease. There is a great growing interest in the domain of deep learning techniques for identifying and classifying images with various dataset. This deep learning project is based on a user interface and its application of the Detrozen real life. It will also describe how the system will perform and under what it must operate. In this document, the user interface will also be shown. Both the stakeholders(users) and the developers of the interface can benefit from this approach.

Keywords: Disease Detection; Feature Selection; Convolutional Neural Network; Deep Learning; Tensorflow (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390283
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i2:id:hcseit2390283

DOI: 10.32628/CSEIT2390283

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