Osteosarcoma Tumor Detection using Transfer Learning Models

TL;DR

This study evaluates the performance of various transfer learning models in detecting osteosarcoma tumors from histopathological images, highlighting InceptionResNetV2's superior accuracy and precision.

Contribution

The paper compares four pre-trained transfer learning models specifically for osteosarcoma tumor detection, demonstrating their effectiveness in a domain with limited large datasets.

Findings

InceptionResNetV2 achieved 93.29% accuracy.

InceptionResNetV2 had the highest precision and recall.

EfficientNetB7 showed significantly lower performance.

Abstract

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.