A DenseNet CNN-based liver lesion prediction and classification for future medical diagnosis

• DOI: 10.1016/j.sciaf.2023.e01629
• Publication Date: 2023-03-11T16:29:53Z
• Authors: N Nanda Prakash; V Rajesh; Dumisani Lickson ...; Sandeep Dwarkanat...; Sk Hasane Ahammad
• Keywords: Confusion matrix; Abnormality; Liver disease

Liver disease diagnosis is a major medical challenge in developing nations. Every year around 30 billion people face liver failure issues resulting their death. The past abnormality detection models have faced less accuracy and high theory of constraint metrics. lesion on the hasn't been identified clearly with earlier models, so an advanced, efficient, effective essential. To overcome limitations existing this approach proposes deep DenseNet convolutional neural network (CNN) based learning technique. This work collected Computed Tomography (CT) scan images from Kaggle dataset for training initial stage. pre-processing has performed region-growing segmentation, through CNN. real-time test are Government General Hospital Vijayawada (10,000 samples), verified proposed CNN to diagnose whether input lesion. Finally, results obtained derived confusion matrix summarizes performance methodology following metrics at 98.34%, sensitivity 99.72%, recall 97.84%, throughput 98.43% rate 93.41%. comparison reveals that technique attains more outperforms other pioneer methodologies.