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

arXiv: https://arxiv.org/abs/2204.03572 · HTML: https://mlsanity.com/paper/2204.03572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [DeepPap: Deep Convolutional Networks for Cervical Cell Classification](https://mlsanity.com/paper/1801.08616/) (2018)
- [Automated Classification of Histopathology Images Using Transfer Learning](https://mlsanity.com/paper/1903.10035/) (2019)

## Abstract (arXiv)

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.