A Novel Soft Clustering Method for Detection of Exudates

Wisaeng, Kittipol

doi:10.32604/csse.2023.034901

Cited by 2 publications

(1 citation statement)

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“…In the case of the MRI dataset, which comprises 253 images, 98 are normal, and 155 are brain tumor images, all of which have undergone a thorough review by three medical experts. The Dice similarity index is calculated as two times the region of the intersection of A and B, divided by the sum of the region of A and B: Dice=2 |A∩B|/(|A|+|B|) =2TP/(2TP+FP+FN) (TP=True Positives, FP=False Positives, and FN=False Negatives) [31], [32]. Note: The count of brain tumor pixels with an intensity of 1 in image A is the number of positives, while the total number of brain tumor pixels with a value of 1 in both A and B is referred to as the number of true positives or TP.…”

Section: ) Dice Similarity Indexmentioning

confidence: 99%

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Wisaeng

2023

IEEE Access

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The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be timeconsuming and delay diagnosis. To address this challenge, researchers have set out to develop an algorithm that can automatically determine whether MRI images contain brain tumors and identify their features. This paper proposes the U-Net++DSM, a collaborative approach combining U-Net++ with Deep Supervision Mechanism (DSM) as its backbone. To enhance the segmentation power of U-Net++DSM, medical professionals have trained a dilation operator using fully annotated images. The results of this method demonstrate that the combination of U-Net++DSM and the dilation operator significantly improves segmentation accuracy, especially when the number of fully-labeled images is limited. The results show that the proposed U-Net++DSM outperforms traditional U-Net models by achieving high segmentation performance, surpassing other state-of-the-art models, with a sensitivity of 98.59.00%, a specificity of 98.72%, an accuracy of 98.64%, and an average Dice score of 98.81% when tested on publicly available databases. Compared to other existing segmentation methods, the U-Net++DSM method has the potential to yield even better brain tumor segmentation results in terms of pixel-based classification and dice similarity performance metrics.

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Section: ) Dice Similarity Indexmentioning

confidence: 99%