Classification and Segmentation on Multi-regional Brain Tumors Using Volumetric Images of MRI with Customized 3D U-Net Framework

Ahamed, Md. Faysal; Islam, Md. Robiul; Hossain, Tahmim; Syfullah, Khalid; Sarkar, Ovi

doi:10.1007/978-981-19-7528-8_18

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Furthermore, identifying polyps from background objects, such as surrounding tissue, might sometimes be challenging. Obtaining colonoscopy videos that were evaluated by endoscopists in clinical facilities can be time-consuming [ 8 ]. This could lead to missed early stage detections.…”

Section: Introductionmentioning

confidence: 99%

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Ahamed,

Syfullah,

Sarkar

et al. 2023

Sensors

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Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

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Section: Introductionmentioning

confidence: 99%

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Ahamed,

Syfullah,

Sarkar

et al. 2023

Sensors

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A novel method for glioma segmentation and classification on pre-operative MRI scans using 3D U-Nets and transfer learning

Srivastava,

Gera,

Rani

et al. 2024

Multimed Tools Appl

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Survey on the Techniques for Classification and Identification of Brain Tumour Types from MRI Images Using Deep Learning Algorithms

B¹,

K²

2023

RACSC

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A t umour is an uncontrolled growth of tissues in any part of the body. Tumours are of different types and characteristics and have different treatments. Detection of a tumour in the earlier stages makes the treatment easier. Scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumours. This paper provides a systematic literature survey of techniques for brain tumour segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques. This survey covers publicly available datasets, enhancement techniques, segmentation, feature extraction, and the classification of three different types of brain tumours that include gliomas, meningioma, and pituitary and deep learning algorithms implemented for brain tumour analysis. Finally, this survey provides all the important literature on the detection of brain tumours with their developments.

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Classification and Segmentation on Multi-regional Brain Tumors Using Volumetric Images of MRI with Customized 3D U-Net Framework

Cited by 3 publications

References 21 publications

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

A novel method for glioma segmentation and classification on pre-operative MRI scans using 3D U-Nets and transfer learning

Survey on the Techniques for Classification and Identification of Brain Tumour Types from MRI Images Using Deep Learning Algorithms

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