Optimization Techniques for Machine Learning

Zouggar, Souad Taleb; Adla, Abdelkader

doi:10.1007/978-981-15-0994-0_3

Cited by 4 publications

(1 citation statement)

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“…Image augmentation is only used on the training dataset and not on the validation or test datasets. On the other hand, data preparation must be done consistently across all model datasets 49 . The data was then synthesized using a combination of affine image transformations, including rotation, shifting, scaling (zoom in/out), and flipping.…”

Section: Resultsmentioning

confidence: 99%

Computer-aided Diagnosis System for Grading Brain Tumor Using Histopathology Images Based on Color and Texture Features

Elazab

Gab-Allah

Elmogy

2022

Preprint

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Cancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). The purpose of this paper is to create an automated diagnostic system based on brain cancer histopathology images. In this paper, several imaging characteristics, including conventional intensity and advanced texturing features (grey co-occurrence, gray-level run-length matrix, and local binary pattern), were included in the training of a hybrid ensemble classification model. The textural and color characteristics were validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 94.6%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules.

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Section: Resultsmentioning

confidence: 99%