Osteosarcoma Tumor Detection using Transfer Learning Models
- URL: http://arxiv.org/abs/2305.09660v1
- Date: Tue, 16 May 2023 17:58:29 GMT
- Title: Osteosarcoma Tumor Detection using Transfer Learning Models
- Authors: Raisa Fairooz Meem, Khandaker Tabin Hasan
- Abstract summary: This paper studies the performance of transfer learning models for osteosarcoma tumour detection.
InceptionResNetV2 achieved the highest accuracy (93.29%), followed by NasNetLarge (90.91%), ResNet50 (89.83%) and EfficientNetB7 (62.77%)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas.
This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
arXiv Detail & Related papers (2024-09-29T07:04:26Z) - Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks [11.749248917866915]
We propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types.
We pretrained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients.
The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%.
arXiv Detail & Related papers (2024-07-11T16:03:59Z) - TotalSegmentator: robust segmentation of 104 anatomical structures in CT
images [48.50994220135258]
We present a deep learning segmentation model for body CT images.
The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
arXiv Detail & Related papers (2022-08-11T15:16:40Z) - Examining the behaviour of state-of-the-art convolutional neural
networks for brain tumor detection with and without transfer learning [0.0]
Two different kinds of dataset are investigated using state-of-the-art CNN models in this research work.
The EfficientNet-B5 architecture outperforms all the state-of-the-art models in the binary-classification dataset with the accuracy of 99.75% and 98.61% accuracy for the multi-class dataset.
arXiv Detail & Related papers (2022-06-02T18:49:28Z) - Osteoporosis Prescreening using Panoramic Radiographs through a Deep
Convolutional Neural Network with Attention Mechanism [65.70943212672023]
Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs.
dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
arXiv Detail & Related papers (2021-10-19T00:03:57Z) - Medulloblastoma Tumor Classification using Deep Transfer Learning with
Multi-Scale EfficientNets [63.62764375279861]
We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.
Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
arXiv Detail & Related papers (2021-09-10T13:07:11Z) - A Deep Learning Study on Osteosarcoma Detection from Histological Images [6.341765152919201]
The most common type of primary malignant bone tumor is osteosarcoma.
CNNs can significantly decrease surgeon's workload and make a better prognosis of patient conditions.
CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance.
arXiv Detail & Related papers (2020-11-02T18:16:17Z) - Classification of COVID-19 in CT Scans using Multi-Source Transfer
Learning [91.3755431537592]
We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans.
With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet.
Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
arXiv Detail & Related papers (2020-09-22T11:53:06Z) - An interpretable classifier for high-resolution breast cancer screening
images utilizing weakly supervised localization [45.00998416720726]
We propose a framework to address the unique properties of medical images.
This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions.
It then applies another higher-capacity network to collect details from chosen regions.
Finally, it employs a fusion module that aggregates global and local information to make a final prediction.
arXiv Detail & Related papers (2020-02-13T15:28:42Z) - Predictive modeling of brain tumor: A Deep learning approach [0.0]
This paper presents a Convolutional Neural Network (CNN) based transfer learning approach to classify the brain MRI scans into two classes using three pre-trained models.
Experimental results show that the Resnet-50 model achieves the highest accuracy and least false negative rates as 95% and zero respectively.
arXiv Detail & Related papers (2019-11-06T09:27:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.