Machine Learning in the Diagnosis of Follicular Lymphoma

Souza, Lucas Lacerda de; Araujo, André; Silva, Viviane Mariano da; Santos‐Silva, Alan Roger; Lopes, Márcio Ajudarte; Moraes, Matheus Cardoso; Vargas, Pablo Agustín

doi:10.1016/j.oooo.2023.03.056

Cited by 1 publication

(1 citation statement)

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“…A kernel called the radial basis function 44 has been used to tell the difference between the three types of follicular tissue. In the previous research, classifiers like Bayesian parameter estimation, non‐grid features‐based registration, 22 linear discriminant analysis, 33 Laplacian eigen map, 34 Gaussian mixer model, 35 neuro‐fuzzy inference system, 35 rule‐based classification, 37 Deep learning approaches like U‐net, 41 Alexnet, 42 transfer learning, 46 genetic algorithm, 7 bayesian neural network, 18 DCGAN, 47 multilayer perceptron, 18 Linear regression tree, 8 ensembled models, 8 and other machine learning approaches 9,45,46,51 …”

Section: Computer‐based Analysis Technique For Flmentioning

confidence: 99%

Review of computer‐assisted diagnosis model to classify follicular lymphoma histology

Saxena,

Aggarwal,

Sinha

et al. 2024

Cell Biochemistry & Function

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The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.

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