Revolutionizing Cancer Diagnosis in IoMT with a Novel Lightweight Deep Learning Model for Histopathological Image Classification

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dc.contributor.authorPatel, Warish
dc.contributor.authorKoyuncu, Hakan
dc.contributor.authorGanatara, Amit
dc.date.accessioned2025-02-06T18:01:19Z
dc.date.available2025-02-06T18:01:19Z
dc.date.issued2023
dc.departmentAltınbaş Üniversitesien_US
dc.description2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023 -- 7 December 2023 through 9 December 2023 -- Dubai -- 198251en_US
dc.description.abstractOur proposed solution for breast cancer diagnosis offers a highly reliable and efficient alternative to the traditional manual examination method. By utilizing deep learning models designed with Convolutional Neural Networks (CNN), We have created a breast cancer detection system that is automated, inexpensive, and easy to transport. A system that significantly reduces the risk of delayed diagnosis and saves lives. This approach relies on an integrated ultrasensitive micro-bio-heat sensor array structured as a 3x3 grid using Altium software. The array functions as an embedded system aiming for the early identification of breast cancer. Leveraging IoT advancements, this system can connect with a server through smartphones. This study demonstrates the effectiveness of switch getting to know in histopathological photo classification. This research assesses the effectiveness of CNN models, both with and without transfer learning. It uses a pre-Trained VGG16 model for image classification and demonstrates its successful implementation on a Raspberry Pi. This highlights its efficiency when running on a lightweight and portable processor. Our experimental results show that our system achieves an accuracy of 78% on the BreakHis database and can run on a Raspberry Pi device with minimal resources. © 2023 IEEE.en_US
dc.identifier.doi10.1109/MoSICom59118.2023.10458800
dc.identifier.endpage257en_US
dc.identifier.isbn979-835039341-5
dc.identifier.scopus2-s2.0-85190115921
dc.identifier.startpage252en_US
dc.identifier.urihttps://doi.org/10.1109/MoSICom59118.2023.10458800
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5319
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings of IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250206
dc.subjectAutomated detectionen_US
dc.subjectBreast cancer diagnosisen_US
dc.subjectConvolutional Neural Networks (CNN)en_US
dc.subjectDeep learningen_US
dc.subjectVGG16 modelen_US
dc.titleRevolutionizing Cancer Diagnosis in IoMT with a Novel Lightweight Deep Learning Model for Histopathological Image Classificationen_US
dc.typeConference Objecten_US

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