Multimodal Disease Detection Using Deep Learning

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Year : 2025 | Volume : 16 | 02 | Page : –
    By

    Aman Kumar,

  • Aman Wadhwani,

  • Priyanshu,

  1. Student, Department of Computer Science & Engineering, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India
  2. Student, Department of Computer Science & Engineering, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India
  3. Student, Department of Computer Science & Engineering, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India

Abstract

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Artificial Intelligence (AI) is playing an increasingly pivotal role in modern healthcare, particularly in improving the speed and accuracy of disease detection. With the evolution of Machine Learning (ML), Deep Learning (DL), and high-performance computing, AI-based solutions are now capable of processing extensive medical datasets—ranging from patient records to diagnostic images—with remarkable efficiency. These systems offer immense potential for early intervention, improved clinical decision-making, and alleviating pressure on overburdened healthcare systems. This study presents the design and assessment of intelligent models for multi-disease detection using a combination of classical machine learning and advanced deep learning techniques. Curated datasets, including structured clinical data and medical imaging (such as chest X-rays, ultrasounds, and MRIs), were utilized for model training and validation. The proposed framework incorporates a variety of algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), VGG, XGBoost, Random Forest, ResNet, and DenseNet, each selected for their strengths in handling specific types of data and tasks. The models were evaluated using key performance indicators such as accuracy, precision, recall, F1-score, and ROC-AUC. Among the tested models, ResNet and DenseNet demonstrated superior effectiveness in processing image-based data, showing high diagnostic accuracy and generalization capability. By automating crucial aspects of diagnosis, the system aims to reduce human error, support consistent decision-making, and accelerate the clinical workflow. Additionally, the approach supports the development of scalable, web-accessible platforms that can serve both healthcare professionals and patients in remote or resource-limited areas. This research contributes to the growing field of AI-assisted healthcare and offers a foundation for future advancements in diagnostic technology.

Keywords: CNN, XGBoost, VGG, Random Forest, DenseNet, SVM, ResNet

How to cite this article:
Aman Kumar, Aman Wadhwani, Priyanshu. Multimodal Disease Detection Using Deep Learning. Journal of Computer Technology & Applications. 2025; 16(02):-.
How to cite this URL:
Aman Kumar, Aman Wadhwani, Priyanshu. Multimodal Disease Detection Using Deep Learning. Journal of Computer Technology & Applications. 2025; 16(02):-. Available from: https://journals.stmjournals.com/jocta/article=2025/view=0


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Ahead of Print Subscription Original Research
Volume 16
02
Received 22/05/2025
Accepted 24/05/2025
Published 12/07/2025
Publication Time 51 Days
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