Target Driven Autoencoder: A Supervised Learning Approach for Tumor Segmentation

Das, Abhishek; Mohapatra, Saumendra Kumar; Pattanaik, Rakesh Kumar; Tripathy, Balaram; Patra, Gyana Ranjan; Mohanty, Mihir Narayan

doi:10.1109/esic60604.2024.10481532

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Preprint1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Segmentation of MR images for brain tumor detection using autoencoder neural network

Hoseini,

Shamlou,

Ahmadi-Gharehtoragh

2024

Discov Artif Intell

View full text Add to dashboard Cite

Segmentation of MR images for brain tumor detection using autoencoder neural network

Hoseini,

Shamlou,

Ahmadi-Gharehtoragh

2024

Discov Artif Intell

View full text Add to dashboard Cite

Segmentation of MR Images for Brain Tumor Detection Using Autoencoder Neural Network

Hoseini,

Shamlou,

Ahmadi-Gharehtoragh

2024

Preprint

View full text Add to dashboard Cite

Medical images often require segmenting into different regions in the first analysis stage. Relevant features are selected to differentiate various regions from each other, and the images are segmented into meaningful (anatomically significant) regions based on these features. The purpose of this study is to present a model for segmenting and identifying the local tumor formation in MR images of the human brain. The proposed system operates in an unsupervised manner to minimize the intervention of expert users and to achieve an acceptable speed in the tumor classification process. The proposed method includes several steps of preprocessing for different brain image classify that Perform the normalization task. These preprocessing steps lead to more accurate results in high-resolution images and ultimately improve the accuracy and sensitivity of tumor separation from brain tissue. The output of this stage is applied to a self-encoding neural network for image zoning. By nature of self-encoding networks, leads to reduce the dimensionality of tumor pixels from the surrounding healthy environment, which significantly helps remove regions incorrectly extracted as tumors. Finally, by extracting features from the previous stage's output through Otsu thresholding, the surrounding area and type of tumor are also extracted. The proposed method was trained and tested using the BRATS2020 database and evaluated by various performance metrics. The results based on the Dice Similarity Coefficient (DSC) show an accuracy of 97% for the entire MR image and improved detection accuracy compared to other methods, as well as a reduction in the cost of the diagnostic process.

show abstract

Target Driven Autoencoder: A Supervised Learning Approach for Tumor Segmentation

Cited by 2 publications

References 18 publications

Segmentation of MR images for brain tumor detection using autoencoder neural network

Segmentation of MR images for brain tumor detection using autoencoder neural network

Segmentation of MR Images for Brain Tumor Detection Using Autoencoder Neural Network

Contact Info

Product

Resources

About