Assessment of experimental OpenCV tracking algorithms for ultrasound videos

Levin, A. A.; Klimov, D. D.; Nechunaev, A.; Prokhorenko, L. S.; Mishchenkov, Denis; Nosova, A. G.; Astakhov, D.; Poduraev, Yu. V.; Панченков, Д. Н.

doi:10.1038/s41598-023-30930-3

Cited by 5 publications

(2 citation statements)

References 33 publications

(29 reference statements)

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“…First, using the Python OpenCV package, 43 the original photos were downscaled to 500 × 500 and turned to grayscale. Black Hat morphological filtering 44 using cv2.MORPH BLACKHAT was used to identify the hair and artifact contours, with a 17 × 17 size kernel and one iteration.…”

Section: Preprocessingmentioning

confidence: 99%

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Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Akram,

Rashid,

Jaffar

et al. 2023

Skin Research and Technology

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IntroductionParticularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques.MethodThis research uses a hybrid deep learning model that combines two cutting‐edge approaches: Mask Region‐based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset.ResultsThe experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state‐of‐the‐art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well.ConclusionIn conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

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Section: Preprocessingmentioning

confidence: 99%

“…The accuracy on the ISIC-2019 dataset was 89.57%. Hyperspectral image engineering for skin cancer classification was revolutionized by Huang et al43 in 2023 with the release of YOLOv5. Unlike previous approaches, this one was validated on a dataset the authors developed, achieving an accuracy of 79.20%.…”

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confidence: 99%