Titlebook: Intelligent Computing in Carcinogenic Disease Detection; Kaushik Das Sharma,Subhajit Kar,Madhubanti Maitra Book 2024 The Editor(s) (if app

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Classical Approaches in Gene Evaluation for Carcinogenic Disease Detection,n the human body through gene expression information. Microarray data sets are critical to the automated detection of carcinogenic diseases, as they facilitate manual pathological diagnosis methods. The challenges arise from the vast number of genes within limited accessible samples, making the anal

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Intelligent Computing Approach for Leukocyte Identification,volves identifying leukocytes using color-based clustering and then extracting a large set of features from the detected cells. Weighted aggregation-based transposition PSO (WATPSO) is introduced that effectively selects an optimized subset of features that are necessary for correctly classifying he

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Ventilator

Conclusion,ormation at the gene, cell, and tissue levels. The suggested frameworks achieve strong outcomes in microscopic image investigations and DNA microarray data processing by employing intelligent assessment criteria. Important contributions include the study of gene subset identification, improved gene

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Biological Background of Benchmark Carcinogenic Data Sets,ssential for detecting and classifying carcinogenic diseases. This chapter explores the in-depth procedure for preparing CT scan, microscopic blood smear image, and microarray gene expression data. Additionally, standard benchmark data sets for these modalities are discussed, contributing to the adv

LAIR

Classical Approaches in Gene Evaluation for Carcinogenic Disease Detection,AML, and MLL subclasses of cancer, the chapter seeks to distinguish cancer types based on patterns of gene expression. For a diagnosis to be both accurate and efficient, filter- and wrapper-based gene selection techniques have been examined. The chapter delves more into gene ranking techniques based

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Intelligent Computing Approach for Lung Nodule Detection,odology has been successfully applied to the Lung Image Database Consortium (LIDC) data set. The experimental findings demonstrate the efficacy of the proposed method, with only 12 differentiating features required to achieve a remarkable sensitivity of 97.59% and blind testing accuracy of 97.78%. C

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