Earticle

초록

영어

Breast cancer remains the foremost cause of cancer-related mortality worldwide. The histopathological diagnosis is impeded by the intricate nature of image interpretation and the presence of inter-observer variability among pathologists. Deep learning (DL) for cancer image understanding has revolutionized accurate breast cancer diagnosis, marking a significant advancement in medical image analysis. Researchers proposed DL-based intelligent models to overcome the challenges of manual observations. However, the existing models suffer from a considerable computational burden, demanding substantial time investments that restrict efficient and scalable breast cancer diagnosis solutions. Our study introduces an automated breast cancer diagnosis system employing a lightweight Convolutional Neural Network (CNN) model, adept at extracting intricate features from histopathological images. Our system has attained superior accuracy through extensive experimentation on a comprehensive breast cancer dataset while employing fewer parameters compared to state-of-theart (SOTA) techniques.

목차

Abstract
I. INTRODUCTION
II. PROPOSED METHODOLOGY
A. Problem formulation
B. Model
C. Feature Fusion
III. RESULTS AND DISCUSSION
A. Dataset
B. Evaluation Matrices
C. Comparison with SOTA Methods
D. Qualitative results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Muhammad Nadeem [ Department of Computer Science & Engineering Sejong Univerity Seoul, Republic of Korea ]
• Haseeb Khan [ Northwest School of Medicine Peshawar, Khyber Pakhtunkhwa, Pakistan ]
• Wisal Khan [ Northwest School of Medicine Peshawar, Khyber Pakhtunkhwa, Pakistan ]
• L. Minh Dang [ Department of Artificial Intelligence Sejong University Seoul, Republic of Korea ]
• Nguyen Le Quan [ Department of Computer Science and Engineering Sejong Uviversity Seoul, Republic of Korea ]
• Hyeonjoon Moon [ Department of Computer Science and Engineering Sejong Uviversity Seoul, Republic of Korea ] Corresponding Author

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