International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
Deep Learning For Medical Analysis: A Systematic Survey Of Techniques, Application, And futures Directions. Prof. S. B. Bele1, Neha G. Parshette2, Riyasha G. Sonawane3, Sakshi R. Jadhav4, Jesika P. Chourpagar5. 1Prof. S. B. Bele (MCA Department Of Vidya Bharati Mahavidyalaya, Amravati ). 2Neha G. Parshette, 3Riyasha G. Sonawane, 4Sakshi R. Jadhav (Students, MCA Department Of Vidya Bharati
Mahavidyalaya, Amravati).
5Jesika P. Chourpagar (Student, Department Of Computer Science, Vidya Bharati Mahavidyalaya, Amravati).
------------------------------------------------------------------------***----------------------------------------------------------------------highlights significant challenges, and describes promising Abstract: Deep learning transformed healthcare by areas of future development. It aims to offer a technical and practical overview of how deep learning is advancing healthcare's future.
extending beyond conventional computer-aided diagnosis (CAD) schemes. Rather than depending on handcrafted features, deep models learn raw data, driving innovation in various areas such as oncology, neurology, and pathology. Models are employed for important tasks including image classification, segmentation, detection, and multimodal analysis. Although strong, this technology is confronted by a number of challenges such as data availability, interpretability, and ethics. Future directions include Explainable AI (XAI) to establish confidence, federated learning to maintain patient privacy, multimodal fusion for integrated patient models, and digital twins to treat virtually. Successful integration of AI in medicine ultimately hinges on collaboration, rigorous regulations, and trust between clinicians and AI systems.
2. Background on Deep Learning in Medicine: 2.1 Convolutional Neural Networks (CNNs): CNNs are the bedrock of clinical imaging. Their capacity for recording spatial hierarchies means that they are perfectly suited to disease classification and localization. ResNet, Dense Net, and Efficient Net architectures are particularly popular. For instance, CNN-powered COVID-19 models were able to reach accuracy levels above 95% on chest Xrays, illustrating their capability in high-speed triage applications. 2.2 Recurrent Neural Networks (RNNs) and LSTMs:
Keywords: AI in healthcare, CAD, medical imaging, oncology, neurology, pathology, segmentation, XAI, federated learning, digital twins.
Sequential clinical information like ECG, EEG, and patient monitoring data need to be temporally modelled. LSTMs and gated recurrent units (GRUs) are capable of holding long-term dependencies, allowing the early detection of arrhythmias or seizures. RNNs are utilized in some ICU systems to predict sepsis six hours ahead of time.
1. Introduction: Artificial intelligence (AI) is revolutionizing healthcare in an unprecedented way. The surge in medical data—ranging from MRI and CT scans to electronic health records and genomic data— has presented an opportunity as well as a challenge. Handcrafted features and rule-based systems were used by traditional CAD systems, which were introduced in the late 20th century. Though these systems were revolutionary then, they were not robust, adaptable, or scalable. The introduction of deep learning in 2012 was the big wake-up call. The win by AlexNet in ImageNet competition showed the capability of CNNs to learn subtle features directly from raw data. Within a short time, this innovation percolated into the field of healthcare. Deep learning algorithms now identify diabetic retinopathy with the same performance as ophthalmologists, identify lung nodules in CT scans, and isolate tumors in brain MRIs with great accuracy. This article summarizes the roots of deep learning in medicine, discusses clinical applications,
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2.3 Autoencoders and VAEs: Autoencoders compact data, filter out noise, and reveal underlying patterns. Variational autoencoders add to these skills with generative modeling, creating synthetic scans
or pathology images for uncommon diseases. 2.4 Generative Adversarial Networks (GANs): GANs create realistic medical images, conduct modality translation (CT-to-MRI), and augment datasets. They are especially beneficial when balancing datasets in uncommon conditions, allowing models to generalize better.
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