LEADER 03903nam 22006975 450 001 9910645893103321 005 20240419053529.0 010 $a981-19-8937-0 024 7 $a10.1007/978-981-19-8937-7 035 $a(MiAaPQ)EBC7184195 035 $a(Au-PeEL)EBL7184195 035 $a(CKB)26027661600041 035 $a(DE-He213)978-981-19-8937-7 035 $a(PPN)267808534 035 $a(EXLCZ)9926027661600041 100 $a20230118d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning in Cancer Diagnostics $eA Feature-based Transfer Learning Evaluation /$fby 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 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (41 pages) 225 1 $aSpringerBriefs in Forensic and Medical Bioinformatics,$x2196-8853 311 08$aPrint version: Arzmi, Mohd Hafiz Deep Learning in Cancer Diagnostics Singapore : Springer,c2023 9789811989360 327 $a1. 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. 330 $aCancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer. 410 0$aSpringerBriefs in Forensic and Medical Bioinformatics,$x2196-8853 606 $aMedical physics 606 $aArtificial intelligence 606 $aCancer$xImaging 606 $aComputational intelligence 606 $aMedical Physics 606 $aArtificial Intelligence 606 $aCancer Imaging 606 $aComputational Intelligence 615 0$aMedical physics. 615 0$aArtificial intelligence. 615 0$aCancer$xImaging. 615 0$aComputational intelligence. 615 14$aMedical Physics. 615 24$aArtificial Intelligence. 615 24$aCancer Imaging. 615 24$aComputational Intelligence. 676 $a610.153 700 $aArzmi$b Mohd Hafiz$01275821 702 $aAbdul Majeed$b Anwar. P. P 702 $aMuazu Musa$b Rabiu 702 $aMohd Razman$b Mohd Azraai 702 $aGan$b Hong-Seng 702 $aMohd Khairuddin$b Ismail 702 $aAb. Nasir$b Ahmad Fakhri 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910645893103321 996 $aDeep Learning in Cancer Diagnostics$94154887 997 $aUNINA