HEp-2 cell classification using binary decision tree approach

Bansal, Divya; Subramaniam, Kamalraj; Nanjundaswamy, H R

doi:10.1109/iecbes.2016.7843503

Cited by 2 publications

(2 citation statements)

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“…In the HEp-2 cell pattern recognition problem [30], BT is used as a classifier. BT was a supervised algorithm for learning.…”

Section: Classifiersmentioning

confidence: 99%

“…In which, the predictors of the training images were analyzed using the Classification and Regression Trees (CART) algorithm to perform a binary split on any predictor variable. The split criterion gain was determined according to the CART algorithm by the ratio of the parent to child node Gini diversity index [30]. The minimum leaf size at which the tree's output was optimum is the minimum observations on the leaf node.…”

Section: Classifiersmentioning

confidence: 99%

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IoMT-Based Automated Diagnosis of Autoimmune Diseases Using MultiStage Classification Scheme for Sustainable Smart Cities

Shivanna

Stephan²,

Al‐Turjman³

et al. 2022

Sustainability

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The resolution of complex medical diagnoses using pattern recognition requires an artificial neural network-based expert system to automate autoimmune disease diagnosis in blood samples. This process is done using image-based computer-aided diagnosis (CAD) to reduce errors in the diagnosis process. This paper describes a Multistage Classification Scheme (MSCS), which uses antinuclear antibody (ANA) tests to identify and classify the existence of autoantibodies in the blood serum that bind to antigens found in the nuclei of mammalian cells. The MSCS classified HEp-2 cells into three stages by using Binary Tree (BT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) as basic blocks. The Indirect Immunofluorescence (IIF) technique is used in the ANA test with Human Epithelial type-2 (HEp-2) cells as substrates. The efficiency of the proposed methodology is assessed using the dataset of ICPR 2016. The intermediate cells (IMC) and positive cells (PC) were separated in Stage 1 prior to preprocessing based on their total strength, and special preprocessing is applied to intermediate cells for improved output, and positive cells are subjected to mild preprocessing. The mean class accuracy (MCA) was 84.9% for intermediate cells and 95.8% for positive cells, although the carefully picked 24 features and SVM classifier were applied. ANN showed better performance by adjusting the weights using the SCGBP algorithm. So, the MCA is 88.4% and 97.1% for intermediate and positive cells, respectively. BT had an MCA of 95.3% for intermediate and 98.6% for positive. In Stage 2, the meta learners BT2, ANN2, and SVM2 were trained for an augmented feature set (24 + 3 results from base learners). Therefore, the performance of BT2, ANN2, and SV M2 was increased by 1.8%, 4.5%, and 4.1% as compared to Stage 1. In Stage 3, the final prediction was performed by majority voting among the results of the three meta learners to achieve 99.1% MCA. The proposed algorithm can be embedded into a CAD framework built for the ANA examination. The proposed model will improve operational efficiency, decrease medical expenses, expand accessibility to healthcare, and improve patient safety in the sector, enabling enterprises to lower unplanned downtime, develop new products or services, increase operational effectiveness, and enhance risk management.

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“…In the HEp-2 cell pattern recognition problem [30], BT is used as a classifier. BT was a supervised algorithm for learning.…”

Section: Classifiersmentioning

confidence: 99%

Section: Classifiersmentioning

confidence: 99%