Revolutionizing Cancer Diagnosis in IoMT with a Novel Lightweight Deep Learning Model for Histopathological Image Classification
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Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Our 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.
Açıklama
2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023 -- 7 December 2023 through 9 December 2023 -- Dubai -- 198251
Anahtar Kelimeler
Automated detection, Breast cancer diagnosis, Convolutional Neural Networks (CNN), Deep learning, VGG16 model
Kaynak
Proceedings of IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023
