A comparative study and implementation of neuro-fuzzy and decision tree for malignant tumour detection system Online publication date: Thu, 03-Nov-2022
by Sanjeev Kumar; Rajesh Kumar Maurya; Sanjay Kumar Yadav; Baij Nath Kaushik
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 23, No. 3/4, 2022
Abstract: Breast cancer is one of the most chronic diseases found in women. There are two types of tumours found in the breasts: malignant and benign. A patient who has more percentage of malignant tumours is suffering from breast cancer. A model based on neuro-fuzzy is proposed to classify the tumour as malignant or benign. The designed system works on various attributes of tumour like tumour thickness, shape, size, etc. The classification process completes in three phases; phase 1 classifies the attributes as cat1 or cat2 on the basis of information gain. Then in phase 2, cat1 attributes are used to select the class of tumour by using the radial bias function neural network while the cat2 attributes use the fuzzy to select the class of tumour. The results of both techniques are collaborated by using the fuzzy inference system in the phase 3. The effectiveness of the technique is easily identified by the results. The results are compared for the accuracy of cancer detection of cat1 and cat2 with neuro-fuzzy system and decision tree.
Online publication date: Thu, 03-Nov-2022
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