Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images

Mohammed Rahman Sherif; Ardhendu Behera

doi:10.1007/978-3-031-68908-6_19

Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images

Mohammed Rahman Sherif, Ardhendu Behera

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding (ISBN) › peer-review

Abstract

Computer-assisted breast cancer detection is a useful and widely used technique that aids pathologists in clinical diagnoses. Presently, identifying breast cancer is primarily reliant on a single imaging trait. This study contributes to the creation of an artificial intelligence model that can forecast information on the density of tumor cells. Multiple AI models have been experimented with for estimating the tumor density, and the best model was selected by comparing the results of all the models and iterations. Our analyses reveal that the Resnet 101 model, which has all layers trainable and by using the learning rate 1e-3, outperforms baselines on normalized histopathological validation picture data than the model obtained by performing hyperparameter tuning. Commonly, doctors or pathologists with extensive experience are frequently advised to analyze a sample and determine the tumor density. The proposed AI model will support the process and will have a significant impact on the time taken for manual analysis, resulting in faster decision-making, and influencing the patient’s waiting time.

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Original language	English
Title of host publication	International Conference on Deep Sciences for Computing and Communications
Subtitle of host publication	Communications in Computer and Information Science
Editors	Annie Uthra R, Kottilingam Kottursammy, Gunasekaran Raja, Ali Kashif Bashir, Utku Kose, Revathi Appavoo, Vimaladevi Madhivanan
Publisher	Springer
Pages	229-240
Number of pages	12
Volume	2177
ISBN (Print)	978-3-031-68907-9
DOIs	https://doi.org/10.1007/978-3-031-68908-6_19
Publication status	Published - 29 Sept 2024

Publication series

Name	Communications in Computer and Information Science

Keywords

Artificial Intelligence
Breast cancer detection
Pathologists
NuCLS dataset
Artificial intelligence
Resnet

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-68908-6_19

Cite this

Sherif, M. R., & Behera, A. (2024). Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images. In A. Uthra R, K. Kottursammy, G. Raja, A. Kashif Bashir, U. Kose, R. Appavoo, & V. Madhivanan (Eds.), International Conference on Deep Sciences for Computing and Communications: Communications in Computer and Information Science (Vol. 2177, pp. 229-240). (Communications in Computer and Information Science). Springer. https://doi.org/10.1007/978-3-031-68908-6_19

Sherif, Mohammed Rahman ; Behera, Ardhendu. / Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images. International Conference on Deep Sciences for Computing and Communications: Communications in Computer and Information Science. editor / Annie Uthra R ; Kottilingam Kottursammy ; Gunasekaran Raja ; Ali Kashif Bashir ; Utku Kose ; Revathi Appavoo ; Vimaladevi Madhivanan. Vol. 2177 Springer, 2024. pp. 229-240 (Communications in Computer and Information Science).

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title = "Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images",

abstract = "Computer-assisted breast cancer detection is a useful and widely used technique that aids pathologists in clinical diagnoses. Presently, identifying breast cancer is primarily reliant on a single imaging trait. This study contributes to the creation of an artificial intelligence model that can forecast information on the density of tumor cells. Multiple AI models have been experimented with for estimating the tumor density, and the best model was selected by comparing the results of all the models and iterations. Our analyses reveal that the Resnet 101 model, which has all layers trainable and by using the learning rate 1e-3, outperforms baselines on normalized histopathological validation picture data than the model obtained by performing hyperparameter tuning. Commonly, doctors or pathologists with extensive experience are frequently advised to analyze a sample and determine the tumor density. The proposed AI model will support the process and will have a significant impact on the time taken for manual analysis, resulting in faster decision-making, and influencing the patient{\textquoteright}s waiting time. This is a preview of subscription",

keywords = "Artificial Intelligence, Breast cancer detection, Pathologists, NuCLS dataset, Artificial intelligence, Resnet",

author = "Sherif, {Mohammed Rahman} and Ardhendu Behera",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",

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doi = "10.1007/978-3-031-68908-6_19",

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Sherif, MR & Behera, A 2024, Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images. in A Uthra R, K Kottursammy, G Raja, A Kashif Bashir, U Kose, R Appavoo & V Madhivanan (eds), International Conference on Deep Sciences for Computing and Communications: Communications in Computer and Information Science. vol. 2177, Communications in Computer and Information Science, Springer, pp. 229-240. https://doi.org/10.1007/978-3-031-68908-6_19

Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images. / Sherif, Mohammed Rahman; Behera, Ardhendu.
International Conference on Deep Sciences for Computing and Communications: Communications in Computer and Information Science. ed. / Annie Uthra R; Kottilingam Kottursammy; Gunasekaran Raja; Ali Kashif Bashir; Utku Kose; Revathi Appavoo; Vimaladevi Madhivanan. Vol. 2177 Springer, 2024. p. 229-240 (Communications in Computer and Information Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding (ISBN) › peer-review

TY - GEN

T1 - Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images

AU - Sherif, Mohammed Rahman

AU - Behera, Ardhendu

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/9/29

Y1 - 2024/9/29

N2 - Computer-assisted breast cancer detection is a useful and widely used technique that aids pathologists in clinical diagnoses. Presently, identifying breast cancer is primarily reliant on a single imaging trait. This study contributes to the creation of an artificial intelligence model that can forecast information on the density of tumor cells. Multiple AI models have been experimented with for estimating the tumor density, and the best model was selected by comparing the results of all the models and iterations. Our analyses reveal that the Resnet 101 model, which has all layers trainable and by using the learning rate 1e-3, outperforms baselines on normalized histopathological validation picture data than the model obtained by performing hyperparameter tuning. Commonly, doctors or pathologists with extensive experience are frequently advised to analyze a sample and determine the tumor density. The proposed AI model will support the process and will have a significant impact on the time taken for manual analysis, resulting in faster decision-making, and influencing the patient’s waiting time. This is a preview of subscription

AB - Computer-assisted breast cancer detection is a useful and widely used technique that aids pathologists in clinical diagnoses. Presently, identifying breast cancer is primarily reliant on a single imaging trait. This study contributes to the creation of an artificial intelligence model that can forecast information on the density of tumor cells. Multiple AI models have been experimented with for estimating the tumor density, and the best model was selected by comparing the results of all the models and iterations. Our analyses reveal that the Resnet 101 model, which has all layers trainable and by using the learning rate 1e-3, outperforms baselines on normalized histopathological validation picture data than the model obtained by performing hyperparameter tuning. Commonly, doctors or pathologists with extensive experience are frequently advised to analyze a sample and determine the tumor density. The proposed AI model will support the process and will have a significant impact on the time taken for manual analysis, resulting in faster decision-making, and influencing the patient’s waiting time. This is a preview of subscription

KW - Artificial Intelligence

KW - Breast cancer detection

KW - Pathologists

KW - NuCLS dataset

KW - Artificial intelligence

KW - Resnet

UR - https://doi.org/10.1007/978-3-031-68908-6_19

U2 - 10.1007/978-3-031-68908-6_19

DO - 10.1007/978-3-031-68908-6_19

M3 - Conference proceeding (ISBN)

SN - 978-3-031-68907-9

VL - 2177

T3 - Communications in Computer and Information Science

SP - 229

EP - 240

BT - International Conference on Deep Sciences for Computing and Communications

A2 - Uthra R, Annie

A2 - Kottursammy, Kottilingam

A2 - Raja, Gunasekaran

A2 - Kashif Bashir, Ali

A2 - Kose, Utku

A2 - Appavoo, Revathi

A2 - Madhivanan, Vimaladevi

PB - Springer

ER -

Sherif MR, Behera A. Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images. In Uthra R A, Kottursammy K, Raja G, Kashif Bashir A, Kose U, Appavoo R, Madhivanan V, editors, International Conference on Deep Sciences for Computing and Communications: Communications in Computer and Information Science. Vol. 2177. Springer. 2024. p. 229-240. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-68908-6_19

Artificial Intelligence-Assisted Breast Cancer Detection Through Tumor Density Estimation from Histopathological Images

Abstract

Publication series

Keywords

UN SDGs

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