Efficient Diagnoses of Breast Cancer Disease Using Deep Learning Technique
Authors: 
Muhammad Ali Raza, Asad Masood Khattak, Wasim Abbas, Muhammad Zubair AsgharAuthors Info & Claims
Muhammad Ali Raza, Asad Masood Khattak, Wasim Abbas, Muhammad Zubair AsgharAuthors Info & ClaimsPages 136 - 143
Abstract
According to WHO 2023 survey, each year more than 2.3 million breast cancer cases are reported. Breast cancer is the either first or second biggest disease in females that is the cause of death in almost 95% of countries. Diagnosing breast cancer in its early stage can be helpful to overcome this disease and result in an increase in the survival chance of the patient. Machine learning (ML) models and well-established methods for encoding categorical data have produced a wide variety of surprising outcomes when used to diagnose breast cancer using datasets that are imbalanced from testing. Early experiments also used an artificial neural network(ANN) to extract characteristics without understanding the sequencing data. In this study, we present a hybrid deep learning (DL) BiLSTM-CNN model, in order to diagnose breast cancer efficiently from patient data. The BiLSTM-CNN model was applied after dataset balancing. Contrasting to previous investigations, the experimental results of our suggested hybrid DL model were outstanding, with an accuracy of 99.3%, a precision of 99%, a recall of 99%, and an F1-score of 99%.
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Published In
April 2024
491 pages
ISBN:9798400717055
DOI:10.1145/3669754
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Published: 30 August 2024
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- Zayed University, UAE
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ICCAI 2024
ICCAI 2024: 2024 10th International Conference on Computing and Artificial Intelligence
April 26 - 29, 2024
Bali Island, Indonesia
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