A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis

• URL: http://arxiv.org/abs/2204.03572v1
• Date: Thu, 7 Apr 2022 16:45:28 GMT
• Title: A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis
• Authors: Karoline da Rocha, Jos\'e C. M. Bermudez, Elena R. C. Rivero, M\'arcio H. Costa
• Abstract summary: The Epithelial Dysplasia (ED) is a tissue alteration commonly present in lesions preceding oral cancer. This study proposes a method to design a low computational cost classification system to support the detection of dysplastic epithelia.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Epithelial Dysplasia (ED) is a tissue alteration commonly present in lesions preceding oral cancer, being its presence one of the most important factors in the progression toward carcinoma. This study proposes a method to design a low computational cost classification system to support the detection of dysplastic epithelia, contributing to reduce the variability of pathologist assessments. We employ a multilayer artificial neural network (MLP-ANN) and defining the regions of the epithelium to be assessed based on the knowledge of the pathologist. The performance of the proposed solution was statistically evaluated. The implemented MLP-ANN presented an average accuracy of 87%, with a variability much inferior to that obtained from three trained evaluators. Moreover, the proposed solution led to results which are very close to those obtained using a convolutional neural network (CNN) implemented by transfer learning, with 100 times less computational complexity. In conclusion, our results show that a simple neural network structure can lead to a performance equivalent to that of much more complex structures, which are routinely used in the literature.

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