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Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Computers, Materials & Continua 2021年第10期69卷 99-128页

作者： tina babu Deepa Gupta Tripty Singh Shahin Hameed Mohammed Zakariah Yousef Ajami Alotaibi Department of Computer Science and Engineering Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluruIndia Department of Pathology MVR Cancer Center and Research InstitutePoolacodeKeralaIndia College of Computer and Information Sciences King Saud UniversitySaudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversitySaudi Arabaia

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each *** work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes:normal,well,moderate,and *** contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature,Gabor wavelet,wavelet moments,HSV histogram,color auto-correlogram,color moments,and morphological features that can be used to characterize different ***,the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest(BO-RF)*** study uses four datasets(two collected from Indian hospitals—Ishita Pathology Center(IPC)of 4X,10X,and 40X and Aster Medcity(AMC)of 10X,20X,and 40X—two benchmark datasets—gland segmentation(GlaS)of 20X and IMEDIATREAT of 10X)comprising multiple microscope *** results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy(97.25%-IPC,94.40%-AMC,97.58%-GlaS,99.16%-Imediatreat),sensitivity(0.9725-IPC,0.9440-AMC,0.9807-GlaS,0.9923-Imediatreat),specificity(0.9908-IPC,0.9813-AMC,0.9907-GlaS,0.9971-Imediatreat)and F-score(0.9725-IPC,0.9441-AMC,0.9780-GlaS,0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset,highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.

关键词： Colon cancer grading texture features color features morphological features feature extraction Bayesian optimized random forest classifier

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