LEADER 03539nam 22006135 450 001 9910482988603321 005 20251107172119.0 010 $a3-030-71975-8 024 7 $a10.1007/978-3-030-71975-3 035 $a(CKB)4100000011949921 035 $a(MiAaPQ)EBC6633340 035 $a(Au-PeEL)EBL6633340 035 $a(OCoLC)1253473620 035 $a(PPN)255882947 035 $a(DE-He213)978-3-030-71975-3 035 $a(EXLCZ)994100000011949921 100 $a20210529d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Machine Learning Approaches in Cancer Prognosis $eChallenges and Applications /$fedited by Janmenjoy Nayak, Margarita N. Favorskaya, Seema Jain, Bighnaraj Naik, Manohar Mishra 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (461 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v204 311 08$a3-030-71974-X 327 $aAdvances in Machine Learning Approaches in Cancer Prognosis -- Data Analysis on Cancer Disease using Machine Learning Techniques -- Learning from multiple modalities of imaging data for cancer detection/diagnosis -- Neural Network for Lung Cancer diagnosis -- Improved Thyroid Disease Prediction Model Using Data Mining Techniques with Outlier Detection -- Automated Breast Cancer Diagnosis Based on Neural Network Algorithms. . 330 $aThis book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed. . 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v204 606 $aComputational intelligence 606 $aMedicine$xResearch 606 $aBiology$xResearch 606 $aMachine learning 606 $aComputational Intelligence 606 $aBiomedical Research 606 $aMachine Learning 615 0$aComputational intelligence. 615 0$aMedicine$xResearch. 615 0$aBiology$xResearch. 615 0$aMachine learning. 615 14$aComputational Intelligence. 615 24$aBiomedical Research. 615 24$aMachine Learning. 676 $a006.31 702 $aNayak$b Janmenjoy 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910482988603321 996 $aAdvanced machine learning approaches in cancer prognosis$92585617 997 $aUNINA