Deep belief network Assisted quadratic logit boost classifier for brain tumor detection using MR images. (March 2023)

• Record Type: Journal Article
• Authors: Vinay Kumar, V.; Grace Kanmani Prince, P.
• Is Part Of: Biomedical signal processing and control. Volume 81(2023)
• Publication Date: 2023-03
• Subjects: Deep Belief Network -- Logit Boost -- MRI Image -- Brain tumor detection -- Extracted features and Likelihood measure; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104415
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Classification is the main work while detecting the brain tumor images. Prior researchers planned to determine the brain tumors via various classification approaches. But, the error rate and detection time of tumor are high. Therefore, brain tumor detection is improved via classification using the Deep Belief Network Assisted Quadratic Logit BoostClassifier (DBNQLBC) technique. The proposed DBNQLBC technique is employed for increasing the accuracy with lesser error and time using classifying the brain images. The features from the input brain MRI images are taken as input in the DBNQLBC technique to carry out the brain tumor detection. DBNQLBC technique comprises the different types of layers namely input layer, hidden layers, and output layer. From the brain MRI images, the input layer gets the features and it is sent to the hidden layer. In the hidden layer, a quadratic logit boostclassifier is applied to classify the extracted features. Boosting classifier uses the quadratic classifier as a sub-classifier to detect the brain tumor through the likelihood measure. The results of sub-classifiers are merged and create a robust one through diminishing logit loss. The Boosting classifier determines the best classification results that provide higher accuracy results. As a result, input MRI images are accurately categorized into normal and abnormal and the outcomes are displayed at the output layer. From this, brain tumor detection is achieved with lower error, time and higher accuracy. Simulation evaluation is carried out using the metrics such as brain tumor detection accuracy, brain tumor detection time, and false-positive rate based on the number of MRI images. The obtained outcomes ensure the presented DBNQLBC technique effectively increases the brain tumor detection accuracy and reduces the time requirement and false-positive rate than the other methods.