<abstract xmlns="http://www.w3.org/1999/xhtml">

<bold>Introduction</bold>: Artificial intelligence (AI), a component of computer science, has the ability to process the multitude of medical data existing in the medical system around the world. The goal of our study is to build an AI model, based on Machine Learning, capable of assisting pathologists around the world in the diagnosis of the basal cell carcinoma of the skin.
<bold>Material and Method</bold>: Our study is represented by the development of a Mask-RCNN (Mask Region-based Convolutional Neural Network) model, for the detection of cells with typical basal cell carcinoma tumoral changes. A number of 258 digitized histological images were used. The images emerged from Hematoxylin&amp;Eosin stained pathology slides, diagnosed with cutaneous basal cell carcinoma between January 2018 and December 2021, at the Pathology Service of the Mureș County Clinical Hospital.
<bold>Results</bold>: All the used images have the unique resolution of 2560x1920 pixels. For the learning process, we divided these images into two datasets: the learning dataset, representing 80% of the total images; and the test dataset, representing 20% of the total images. The AI model was trained using 1000 epochs with a learning rate of 0.00025 and only one classification category: basal cell carcinoma.
<bold>Conclusions</bold>: The AI model successfully identified in 85% of the cases the areas with pathological changes present in the input images.
</abstract>

Introduction: Artificial intelligence (AI), a component of computer science, has the ability to process the multitude of medical data existing in the medical system around the world. The goal of our study is to build an AI model, based on Machine Learning, capable of assisting pathologists around the world in the diagnosis of the basal cell carcinoma of the skin.
Material and Method: Our study is represented by the development of a Mask-RCNN (Mask Region-based Convolutional Neural Network) model, for the detection of cells with typical basal cell carcinoma tumoral changes. A number of 258 digitized histological images were used. The images emerged from Hematoxylin&Eosin stained pathology slides, diagnosed with cutaneous basal cell carcinoma between January 2018 and December 2021, at the Pathology Service of the Mureș County Clinical Hospital.
Results: All the used images have the unique resolution of 2560x1920 pixels. For the learning process, we divided these images into two datasets: the learning dataset, representing 80% of the total images; and the test dataset, representing 20% of the total images. The AI model was trained using 1000 epochs with a learning rate of 0.00025 and only one classification category: basal cell carcinoma.
Conclusions: The AI model successfully identified in 85% of the cases the areas with pathological changes present in the input images.

Artificial intelligence based model for establishing the histopathological diagnostic of the cutaneous basal cell carcinoma

George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures

Department of Anesthesia and Intensive Care

Acta Marisiensis - Seria Medica

This work is licensed under the Creative Commons Attribution 4.0 International License.

Introduction: Artificial intelligence (AI), a component of computer science, has the ability to process the multitude of medical data existing in the medical...

Artificial intelligence based model for establishing the histopathological diagnostic of the cutaneous basal cell carcinoma

Published Online: Jan 07, 2023

Page range: 164 - 171

Received: Aug 08, 2022

Accepted: Sep 02, 2022

DOI: https://doi.org/10.2478/amma-2022-0020

Keywords
histopathology, AI, Mask-RCNN, cutaneous basal cell carcinoma

© 2022 Andrei Calin Dragomir et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artificial intelligence based model for establishing the histopathological diagnostic of the cutaneous basal cell carcinoma

Published Online: Jan 07, 2023

Page range: 164 - 171

Received: Aug 08, 2022

Accepted: Sep 02, 2022

DOI: https://doi.org/10.2478/amma-2022-0020

Keywordshistopathology, AI, Mask-RCNN, cutaneous basal cell carcinoma

© 2022 Andrei Calin Dragomir et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
histopathology, AI, Mask-RCNN, cutaneous basal cell carcinoma