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@article{179102,
        author = {Mukthadesai and Aishwarya CN and Bhavana P M and Deepika H R and Chandana S J},
        title = {Deep Learning-Based Detection of Liver Cirrhosis Using Convolutional Neural Networks and TensorFlow},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7618-7624},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179102},
        abstract = {Liver cirrhosis is a chronic and progressive condition marked by irreversible scarring and fibrotic remodeling of liver tissues. Over time, this damage significantly compromises liver function and can result in severe, life-threatening complications if left untreated. Early and accurate detection is crucial, as timely medical intervention can improve prognosis and reduce the risk of advanced liver failure. Conventional diagnostic techniques, such as liver biopsies and expert-dependent imaging assessments, often involve invasive procedures, are time-intensive, and can be influenced by subjective judgment.
This research proposes a fully automated detection system for liver cirrhosis using Convolutional Neural Networks (CNNs) implemented within the TensorFlow framework. The system is developed using a supervised learning approach, where a custom dataset of liver images is annotated and labeled as either cirrhotic or non-cirrhotic. The deep CNN architecture is capable of autonomously learning and extracting intricate spatial patterns directly from the input images, eliminating the need for manual feature engineering.
To ensure the robustness of the model and mitigate overfitting, multiple image preprocessing and augmentation strategies were applied, including normalization, image rotation, and horizontal flipping. The dataset was divided into training and validation sets, and performance was assessed using metrics such as precision, recall, F1-score, and overall accuracy. The model exhibited strong classification performance, indicating its effectiveness in identifying cirrhosis.
This study demonstrates the potential of deep learning in medical image analysis and underscores the practicality of deploying CNN-based systems for non-invasive, efficient, and scalable liver disease screening, particularly in areas with limited access to specialized healthcare facilities.},
        keywords = {Machine Learning (ML), Convolutional Neural Networks and Deep Learning), TensorFlow, Medical Image Classification, Liver Cirrhosis Detection, Image Processing, Healthcare AI, Medical Diagnostics.},
        month = {May},
        }