Intelligent and nature inspired optimization methods in medicine: the Pap smear cell classification problem

Abstract

The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. Feature selection is widely used as the first stage of the classification task to reduce the dimension of the problem, decrease noise and improve speed by the elimination of irrelevant or redundant features. The present paper deals with the optimization of nearest neighbour classifiers via intelligent and nature inspired algorithms for a very significant medical problem, the Pap smear cell classification problem. The algorithms used include tabu search, genetic algorithms, particle swarm optimization and ant colony optimization. The proposed complete algorithmic scheme is tested on two sets of data. The first consists of 917 images of Pap smear cells and the second set consists of 500 images, classified carefully by expert cyto-technicians and doctors. Each cell is described by 20 numerical features, and the cells fall into seven classes representing a variety of normal and abnormal cases. Nevertheless, from the medical diagnosis viewpoint, a minimum requirement corresponds to the general two-class problem of correct separation between normal and abnormal cells.

Feature selection using genetic algorithm for classification of schizophrenia using fMRI data

“…corresponding feature is not selected, and if the bit is equal to 1 means the feature is selected [24]. This is the simplest and most straightforward representation scheme.…”

Optimizing the operating conditions in a high precision industrial process using soft computing techniques

“…A large number of new applications with very large input spaces need space dimensionality reduction critically for the efficiency and efficacy of the predictors. Some of these applications include new and classical topics such as bioinformatics [DNA microarrays (Kim & Cho, 2006;Gonzalez et al, 2009), remote sensing multi-and hyperspectral imagery (Malpica et al, 2008), pattern recognition [e.g., handwriting recognition (Su et al, 2009)], text processing (Valeriana-Garcia et al, 2008), Web mining (Chen et al, 2009), speech processing (Avci, 2007;Mostafa & Billor, 2009), artificial vision (Raducanu et al, 2010), medical applications (Marinakis et al, 2009;Wolczowski & Kurzynski, 2010), industrial applications (Avci et al, 2009)].…”

CerviFormer: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer

“…10,11 A significant number of images can be processed by the computerized system, which is beneficial for clinical monitoring, tailored medication, and comparative study. 12 Deep learning (DL) methods are being widely employed in diverse domains, including medical imaging, computer vision, natural language processing (NLP), and so forth. [13][14][15][16][17][18][19][20] The fundamental drawback of conventional CAD systems is the need for manual feature extraction.…”

“…Computer‐aided detection (CAD) techniques are widely being used to examine pap smears quickly and reliably in place of manual diagnosis 10,11 . A significant number of images can be processed by the computerized system, which is beneficial for clinical monitoring, tailored medication, and comparative study 12 . Deep learning (DL) methods are being widely employed in diverse domains, including medical imaging, computer vision, natural language processing (NLP), and so forth 13–20 .…”