Classification of Malignant and Benign tissue with Logistic Regression

Seraj, Raihan; Rezanur, Razib Bin Hasan; Hasib, Mohammad Abdul

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dc.contributor.author	Seraj, Raihan
dc.contributor.author	Rezanur, Razib Bin Hasan
dc.contributor.author	Hasib, Mohammad Abdul
dc.date.accessioned	2022-04-25T08:04:13Z
dc.date.available	2022-04-25T08:04:13Z
dc.date.issued	2015-11-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1405
dc.description	Supervised by Prof. Dr. Mohammad Rakibul Islam, Department of Electrical and Electronic Engineering Islamic University of Technology Organisation of Islamic Cooperation (OIC) Gazipur-1704, Dhaka, Bangladesh	en_US
dc.description.abstract	Detection of breast cancer is the major phase in Cancer Diagnosis. So, classifiers with higher accuracy are always superior. A classifier already carrying high accuracy and then leading it to higher accuracy offers very less chance to a patient to be wrongly classified. This book investigates the use of a modified and improved version of the hypothesis used in the logistic regression. Both gradient descent and advanced optimization techniques are used for the minimization of the cost function. A weighting factor β was assigned in the hypothesis which is a sigmoid function. The dependency of this weighting factor to the number of features, the size of the dataset and the type of optimization technique used were observed. The accuracy was improved significantly by appropriately choosing the value of β, which, is a function of both the number of features and the type of optimization techniques used. The obtained results using the weights were promising, resulting in a significant increase in accuracy, sensitivity, and specificity.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Classification of Malignant and Benign tissue with Logistic Regression	en_US
dc.type	Thesis	en_US