Deep Learning in Cancer Diagnostics [[electronic resource] ] : A Feature-based Transfer Learning Evaluation / / by 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

• Autore: Arzmi Mohd Hafiz
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (41 pages)
• Disciplina: 610.153
• Soggetto topico: Medical physics; Artificial intelligence; Cancer - Imaging; Computational intelligence; Medical Physics; Artificial Intelligence; Cancer Imaging; Computational Intelligence
• Persona (resp. second.): Abdul MajeedAnwar. P. P; Muazu MusaRabiu; Mohd RazmanMohd Azraai; GanHong-Seng; Mohd KhairuddinIsmail; Ab. NasirAhmad Fakhri
• Nota di contenuto: 1. Epidemiology, detection and management of cancer -- 2. A VGG16 feature-based Transfer Learning Evaluation for the diagnosis of Oral Squamous Cell Carcinoma (OSCC) -- 3. The Classification of Breast Cancer: The effect of hyperparameter optimisation towards the efficacy of feature-based transfer learning pipeline -- 4. The Classification of Lung Cancer: A DenseNet feature-based Transfer Learning Evaluation -- 5. Skin Cancer Diagnostics: A VGG Ensemble Approach -- 6. The Way Forward.
• Sommario/riassunto: 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.
• Titolo autorizzato: Deep Learning in Cancer Diagnostics
• ISBN: 981-19-8937-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910645893103321
• Lo trovi qui: Univ. Federico II
• Serie: SpringerBriefs in Forensic and Medical Bioinformatics, . 2196-8853