LEADER 04047nam 22007455 450 001 9910683352603321 005 20251009075045.0 010 $a9789811988141$b(electronic bk.) 010 $z9789811988134 024 7 $a10.1007/978-981-19-8814-1 035 $a(MiAaPQ)EBC7219051 035 $a(Au-PeEL)EBL7219051 035 $a(OCoLC)1373984434 035 $a(DE-He213)978-981-19-8814-1 035 $a(PPN)269094717 035 $a(CKB)26309460200041 035 $a(EXLCZ)9926309460200041 100 $a20230323d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConvolutional Neural Networks for Medical Applications /$fby Teik Toe Teoh 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (103 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$aPrint version: Teoh, Teik Toe Convolutional Neural Networks for Medical Applications Singapore : Springer Singapore Pte. Limited,c2023 9789811988134 320 $aIncludes bibliographical references and index. 327 $a1) Introduction -- 2) CNN for Brain Tumor classification -- 3) CNN for Pneumonia image classification -- 4) CNN for White Blood Cell classification -- 5) CNN for Skin Cancer classification -- 6) CNN for Diabetic Retinopathy detection. 330 $aConvolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aComputer vision 606 $aMedical sciences 606 $aArtificial intelligence 606 $aMachine learning 606 $aImage processing 606 $aArtificial intelligence$xData processing 606 $aComputer Vision 606 $aHealth Sciences 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aImage Processing 606 $aData Science 615 0$aComputer vision. 615 0$aMedical sciences. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aImage processing. 615 0$aArtificial intelligence$xData processing. 615 14$aComputer Vision. 615 24$aHealth Sciences. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aImage Processing. 615 24$aData Science. 676 $a616.0754 700 $aTeoh$b Teik Toe$01216205 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910683352603321 996 $aConvolutional Neural Networks for Medical Applications$93087038 997 $aUNINA