Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.
Author(s): Mohd Hafiz Arzmi, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong-Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab. Nasir
Series: SpringerBriefs in Applied Sciences and Technology: Forensic and Medical Bioinformatics
Edition: 1st ed. 2023
Publisher: Springer
Year: 2023
Language: English
Pages: 40
City: Singapore
Acknowledgements
Contents
1 Epidemiology, Detection and Management of Cancer: An Overview
1.1 Introduction
1.2 Epidemiology, Detection and Management of Breast Cancer
1.3 Epidemiology, Detection and Management of Lung Cancer
1.4 Epidemiology, Detection and Management of Oral Cancer
1.5 Epidemiology, Detection and Management of Skin Cancer
1.6 Conclusion
References
2 A VGG16 Feature-Based Transfer Learning Evaluation for the Diagnosis of Oral Squamous Cell Carcinoma (OSCC)
2.1 Introduction
2.2 Methodology
2.3 Results and Discussion
2.4 Conclusion
References
3 The Classification of Breast Cancer: The Effect of Hyperparameter Optimisation Towards the Efficacy of Feature-Based Transfer Learning Pipeline
3.1 Introduction
3.2 Methodology
3.3 Results and Discussion
3.4 Conclusion
References
4 The Classification of Lung Cancer: A DenseNet Feature-Based Transfer Learning Evaluation
4.1 Introduction
4.2 Methodology
4.3 Results and Discussion
4.4 Conclusion
References
5 Skin Cancer Diagnostics: A VGGEnsemble Approach
5.1 Introduction
5.2 Methodology
5.3 Results and Discussion
5.4 Conclusion
References
6 The Way Forward
6.1 Summary
References
