The field of clinical image analysis has been applying transfer learning models increasingly due to their less computational complexity, better accuracy etc. These are pre-trained models that don't require to be trained from scratch which eliminates the necessity of large datasets. Transfer learning models are mostly used for the analysis of brain, breast, or lung images but other sectors such as bone marrow cell detection or bone cancer detection can also benefit from using transfer learning models, especially considering the lack of available large datasets for these tasks. This paper studies the performance of several transfer learning models for osteosarcoma tumour detection. Osteosarcoma is a type of bone cancer mostly found in the cells of the long bones of the body. The dataset consists of H&E stained images divided into 4 categories- Viable Tumor, Non-viable Tumor, Non-Tumor and Viable Non-viable. Both datasets were randomly divided into train and test sets following an 80-20 ratio. 80% was used for training and 20\% for test. 4 models are considered for comparison- EfficientNetB7, InceptionResNetV2, NasNetLarge and ResNet50. All these models are pre-trained on ImageNet. According to the result, InceptionResNetV2 achieved the highest accuracy (93.29%), followed by NasNetLarge (90.91%), ResNet50 (89.83%) and EfficientNetB7 (62.77%). It also had the highest precision (0.8658) and recall (0.8658) values among the 4 models.
Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images
Austrian Center for Medical Innovation and Technology（奥地利医疗创新与技术中心）；Danube Private University（多瑙河私立大学）；Medical University of Vienna（维也纳医科大学）；Patho im zentrum（中心病理机构）
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning
Institute of Biophysics, National Research Council of Italy（意大利国家研究委员会生物物理研究所）；Institute of Information Science and Technologies, National Research Council of Italy（意大利国家研究委员会信息科学与技术研究所）
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI
Florida Institute of Technology（佛罗里达理工学院）；Daffodil International University（达福德国际大学）；University of North Texas（北德克萨斯大学）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data
Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫·扎耶德儿科外科创新研究所）；Children’s National Hospital（儿童国家医院）；Biomedical Image Technologies（生物医学影像技术中心）；Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN（西班牙生物医学网络研究中心）；ISCIII（卡洛斯三世卫生研究所）；School of Medicine and Health Sciences, George Washington University（乔治·华盛顿大学医学与健康科学学院）
A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study
University of Dhaka（达卡大学）；Utah State University（犹他州立大学）；Noakhali Science and Technology University（诺阿卡利科学技术大学）；Daffodil International University（水仙花国际大学）
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China（电子科技大学光电科学与工程学院）；Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；The Hospital for Sick Children (SickKids)（SickKids 儿童医院（病童医院））
Brain Tumor Classification from MRI Scans via Transfer Learning and Enhanced Feature Representation
Department of CSE, Netrokona University（计算机科学与工程系，Netrokona大学）；Department of CSE, The University of Rajshahi（计算机科学与工程系，拉贾沙希大学）；Department of Information Technology, Allen E. Paulson College of Engineering and Computing, Georgia Southern University（信息技术系，乔治亚南方大学）；Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University（图像与视觉计算中心（CIVC），人工智能学院，人工智能与工程学院，多媒体大学）
A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）；The First Hospital of China Medical University（中国医科大学附属第一医院）；Harvard Medical School（哈佛医学院）
Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer
MSU-BIT-SMBU Joint Research Center of Applied Mathematics, Shenzhen MSU-BIT University（深圳MSU-BIT大学应用数学联合研究中心）；Institute of Control Theory and Control Engineering, School of Automation, Beijing Institute of Technology（北京理工大学控制理论与控制工程研究所）；School of Mathematics and Statistics, Beijing Institute of Technology（北京理工大学数学与统计学学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想集团有限公司）
Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
National University of Computer and Emerging Sciences（国立计算机与新兴科学大学）；MD ANDERSON Cancer Center, The University of Texas（德克萨斯大学MD安德森癌症中心）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
Improved Brain Tumor Detection in MRI: Fuzzy Sigmoid Convolution in Deep Learning
Turku PET Center, Turku University Hospital, University of Turku, Finland（图尔库PET中心，图尔库大学医院，图尔库大学，芬兰）；Turku Intelligent Embedded and Robotic Systems lab, University of Turku, Finland（图尔库智能嵌入式与机器人系统实验室，图尔库大学，芬兰）；School of Information Science and Technology, Fudan University, China（信息科学与技术学院，复旦大学，中国）；Information Sciences and Technology, University at Albany, USA（信息科学与技术，阿尔巴尼大学，美国）；University of Sydney, Australia（悉尼大学，澳大利亚）
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
City University of Hong Kong（香港城市大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of California, San Francisco（加州大学旧金山分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Chinese University of Hong Kong（香港中文大学）；NVIDIA（英伟达）
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment
Viterbi School of Engineering（维特比工程学院）；College of Engineering（工程学院）；University of Southern California（南加州大学）；Rochester Institute of Technology（罗切斯特理工学院）；Northeastern University（东北大学）；School of Arts & Sciences（艺术与科学学院）；State University of New York Plattsburgh（纽约州立大学普拉茨堡分校）
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
Technical University of Berlin, Germany（柏林工业大学（德国））；Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany（波茨坦大学哈索普拉特纳数字工程研究所（德国））；Hasso Plattner Institute for Digital Health at the Icahn School of Medicine at Mount Sinai, NYC, USA（西奈山伊坎医学院哈索普拉特纳数字健康研究所（美国纽约市））
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
School of Mathematics, Physics and Computing, University of Southern Queensland（南方昆士兰大学数学、物理与计算学院）；School of Engineering, University of Southern Queensland（南方昆士兰大学工程学院）
Brain Tumor Classification on MRI in Light of Molecular Markers
Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA Department of Electrical \& Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA University of Maryland, Baltimore County, MD, USA；Princeton University, Princeton NJ 08544, USA, , WWW home page: http://users/ iekeland/web/welcome.html Universit\' e de Paris-Sud, Laboratoire d'Analyse Num\' e rique, B\ a timent 425, F-91405 Orsay Cedex, France
A-QCF-Net: An Adaptive Quaternion Cross-Fusion Network for Multimodal Liver Tumor Segmentation from Unpaired Datasets
University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University（安娜大学工程学院，巴拉特拉桑理工学院校区，安娜大学）；National Institute of Technology（国家理工学院）
Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets
Hamburg University of Technology（汉堡工业大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Research Institute Children’s Cancer Center Hamburg（汉堡儿童癌症中心研究所）
