Intelligent Computing in Carcinogenic Disease Detection

This chapter provides an overview of carcinogenic diseases, commonly known as cancers. When cells in the human body undergo uncontrolled growth, they may form tumors, which can be either cancerous or benign. Carcinogenic cells, characterized by irregular shapes due to DNA mutations, have the ability to metastasis, or spread, to other areas of the body. Reducing mortality rates and providing effective treatment for cancer need early and precise cancer detection. While human perception was the basis for previous diagnosis approaches, new developments in digital imaging tools have brought in a new era where intelligent technology works in conjunction with the medical community. This chapter explores the methods used historically and intelligent aspects of detecting and classifying carcinogenic diseases, emphasizing the significance of early and accurate identification for improved patient outcomes.

This chapter offers a thorough review of current research on the diagnostic prediction of carcinogenic diseases, with a focus on integrating patient-specific information at the gene, cell, and tissue levels. Advanced biological studies and microarray technology provide gene level data, which are essential for intelligent computing in the identification and classification of carcinogenic diseases. These data allow for the simultaneous monitoring of thousands of gene expression levels. Generally, cell level data, which are represented by microscopic blood smear images, have been visually analyzed by experienced technologists. An accurate diagnosis depends on identifying and detecting visual features that are impacted by the staining, smearing, and recording procedures. Tissue level data, exemplified by CT scan images, provide crucial insights into the distribution of physical attributes, essential 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 advancement of research in the field.

This chapter provides an overview of recent advancements in the identification and classification of cancer-causing disorders, focusing on intelligent diagnosis through the application of diverse machine learning techniques. In the last few years, researchers have developed advanced methods that make use of gene, cell, and tissue databases. The chapter provides an overview of the different approaches used in this sector and provides insight into how intelligent technologies are changing the landscape of cancer detection. The significance of these strategies is analyzed, offering insights into how they might improve diagnosis accuracy and advance our understanding of cancer-related disorders.

This chapter explores the critical role that microarray technology plays in computational biology, providing a powerful tool for detecting anomalies in 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 analysis of gene expression levels intricate. Two classical gene selection strategies, namely filter and wrapper methodologies, are explored in this chapter. In filter techniques, genes are ranked statistically, while in wrapper approaches, genes are repeatedly chosen depending on classification outcomes. Although they are predicted to perform better than filter methods, wrapper approaches have a higher computational cost. With a focus on SRBCT, ALL_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 on filter methods with binary and multi-class data sets. Additionally, it elaborates on the utility of Particle Swarm Optimization (PSO) methodology in wrapper-based gene selection from microarray gene expression data, contributing to the understanding of effective diagnostic methodologies.

This chapter addresses the challenge of identifying a small subset of crucial genes from microarray data, given the limited number of effective samples compared to the vast number of genes. The work suggests a novel method combining adaptive k-nearest neighbor-based gene selection with Particle Swarm Optimization (PSO) to obtain accurate identification of cancer subtypes. The aim is to identify a small yet informative set of genes that can be properly classified. In order to effectively explore the right neighborhood and accurate classification, the suggested method incorporates a heuristics for calculating the optimal value of k. Three benchmark microarray data sets namely SRBCT, ALL_AML, and MLL are used to test the suggested gene election method. The method’s effectiveness is demonstrated by the results, which include high classification accuracy on blind test samples, the number of informative genes identified, and computational efficacy. Furthermore, other classifiers, such as Support Vector Machine (SVM), are used to assess the usefulness and universal properties of the identified genes. The results confirm that the suggested methodology is resilient and useful in the field of carcinogenic disease classification.

An automated approach for identifying and classifying leukocytes in microscopic blood smear images have been analyzed in this chapter. The approach involves 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 healthy cells and blast cells. An acute lymphoblastic leukemia (ALL) benchmark data set is utilized to test the suggested methodology. Remarkably, the result showcases a test accuracy of 97.30% with only 6 optimal features chosen by the WATPSO method. The experimental results highlight the effectiveness of this methodology, highlighting the use of few informative features and excellent test accuracy.

The intelligent approaches for the detection and classification of different carcinogenic diseases are presented in this book, with an emphasis on information 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 selection using an adaptive k-NN strategy based on PSO, and sophisticated methods for lung nodule and leukocyte detection and categorization. These methods offer medical professionals a helpful second opinion and perform on par with or better than existing methods. The book also highlights future research possibilities, stressing the value of standardized diagnostic methods and the promise of deep learning networks in biomedical image analysis. It also emphasizes the applicability of microarray technology in clinical microbiology and multimodal medical imaging in advancing cancer research and intelligent diagnostics.