Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images

Abstract

To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.

Liver cancer detection using Artificial Intelligence

“…Moreover, liver cancer detection requires the integration of multiple sources of information, such as clinical data, laboratory tests, and pathology reports. R. V. Manjunath et al [5] using UNet model with accuracy 98.59% proposed modified Unet-60 network which perform segmentation of liver tumor, feature extraction and selection to classify the liver diseases. Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images.…”

Automatic Detection of Liver Diseases based on Supervised Learning Algorithms

“…Using techniques like k-fold cross-validation helps in assessing the model's performance more reliably and ensures that it generalizes well to unseen data; (vi) Regularization: Implementing regularization methods to prevent over tting, which can improve the model's performance on new, unseen data; (vii) Hyperparameter Tuning: Systematically searching for the optimal hyperparameters can ne-tune the model's ability to learn from the data; and (viii) Clinical Validation: Collaborating with healthcare professionals to validate the models in a clinical setting can provide feedback for further re nement. By focusing on these areas, the accuracy and reliability of liver disease detection algorithms can be enhanced, leading to better diagnostic tools in healthcare [7][8][9].…”

“…2. Another research demonstrated a deep learning model for detecting liver disease tumors using CT images, which achieved a dice similarity coe cient value of 98.59%, indicating high accuracy in classi cation [7].…”

“…In [7], several classi ers were compared, including machine learning and deep learning methods, for binary classi cation of liver disease. In [8], authors evaluate various machine learning models for predicting liver disease risk.…”