Malignant melanoma: Underlying epigenetic mechanisms

Sabit, Hussein; Kaliyadan, Feroze; Menezes, Ritesh G.

doi:10.4103/ijdvl.ijdvl_791_19

Cited by 8 publications

(1 citation statement)

References 96 publications

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“…Originating from the malignant transformation of melanocytes, melanoma can spread and metastasize gradually and is the most aggressive and fatal type of skin cancer [1] , and its incidence and lethality have gradually increased in recent decades, with a global average of 160,000 new cases and 48,000 deaths per year. Although malignant melanoma possesses such a high mortality rate, early diagnosis and intervention can dramatically increase patient survival rates [2] and improve poor prognosis.…”

Section: Introductionmentioning

confidence: 99%

IDUNet++: an improved convolutional neural network for melanoma skin lesion segmentation based on UNet++

Zhou

Xing-ri

Niu

2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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Melanoma originates from the malignant transformation of melanocytes, it can gradually spread and metastasize. As the most aggressive and deadly type of skin cancer, melanoma posed a significant threat to patient health, but early diagnosis and intervention can improve patient survival and improve the prognosis of poor patients. Computer-aided diagnosis can help dermatologists to make early diagnoses of melanoma. UNet++ as a more advanced model among existing segmentation algorithms has practical value in the segmentation and diagnosis of melanoma, but after experiments, we found that its segmentation performance still has much room for improvement. In the study, we tried to improve the model performance based on the UNet++ algorithm, and a new convolutional neural network IDUNet++ (Inception Dilated UNet++) for melanoma skin lesion segmentation by introducing Inception block and dilated convolution was proposed. In the segmentation task for the ISIC2016 challenge skin lesion dataset, the model has further improved in segmentation accuracy compared with the original UNet++ model, which obtained 2.88%, 2.66%, 2.66%, 1.03%, 1.03%, and 1.66% in its six evaluation metrics of IoU, Recall, Precision, Accuracy, DICE coefficient and F1-score, respectively.

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Section: Introductionmentioning

confidence: 99%