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Histopathologic Cancer Detection with Hybrid Deep Learning model

Published: 05 April 2024 Publication History

Abstract

Histopathological examination, as the "gold standard" recognized by the medical community, can easily reach the resolution of microns and is a qualitative examination. With the rapid development of computer hardware and software, deep learning has been triggered in the computer-aided diagnosis of medical images. To create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans, we proposed a hybrid deep learning model which combining Resnet and Densenet We combined Resnet and Densenet to extract image features. We introduced some related work about the histopathological examination. In the experiment, we compared our model with other models, the elevation metrics is accuracy and Auc-Roc score. From the definition, the higher Auc Roc Score and accuracy are, the better performance the model will gain. On the modified version of the PatchCamelyon (PCam) benchmark dataset, Our model achieved the highest AUC score (0.971) and highest accuracy (0.982) on the test set.

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    ISAIMS '23: Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
    October 2023
    1394 pages
    ISBN:9798400708138
    DOI:10.1145/3644116
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 05 April 2024

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