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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/nq.2022.20.11.NQ66046


Gururaj L Kulkarni, Sanjeev S Sannakki, Vijay S Rajpurohit


The detection of liver cancer in its early stages is very difficult and more time consuming. The proposed system collects microscopic images as input from the patients and preprocesses them to extract features. Once the feature extraction stage is completed the classification of the image need to be done on them. The proposed system uses the classifier support vector machine (SVM) technique to classify the images into their respective classes. The classifier in the proposed system uses the normal approach of classification i.e., a classifier has normally two stages one is training and then testing. Each of these classifiers goes through both these stages. Firstly, the training stage involves the system learning on the images and their respective category which is already known from the expert advice. In this way a series of images are given in the form of an input with their actual category. The classifier learns from this and then in the testing phase a new image is given for classification to the system. The system uses the prior knowledge which it has learnt during the training phase to predict the category for the image.


Microscopic Images, Support Vector Machine (SVM), Feature Extraction, Training, Testing.

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