Liver Lesions Classification System using CNN with Improved Accuracy


  • Swapnil V.Vanmore
  • Sangeeta R.Chougule



Computer-Aided-Diagnosis(CAD); Relu (Rectified linear unit); Digital Imaging and Communication in Medicine(DICOM);Computed Tomography(CT);Convolution Neural Network(CNN).


In this paper, the liver lesions classification system for CT images use deep learning (CNN)model with
improved accuracy has proposed. The sequential model of CNN architecture with I/P convolution layer, Hidden

convolution layer, and O/P convolution layer for CT images have been used to classify liver lesions. TensorFlow-
2.0 is used to make an image with varying image qualities.The proposed network is used for CT images with an

image size of 65×65, 60×60, 50×50 for which liver lesions classification accuracy of 99%,97%,95% respectively
are achieved. The regularization technique used in proposed N/W has helped to improve the accuracy and
minimization over fitting problem. The classification accuracy improvement has justified by comparing the
proposed research work with other researcher's work.




How to Cite

Swapnil V.Vanmore, & Sangeeta R.Chougule. (2022). Liver Lesions Classification System using CNN with Improved Accuracy. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 4(1).