A Comparative Study of Melanoma Skin Cancer Detection in Traditional and Current Image Processing Techniques

Sreedhar, B.; B.E, Manjunath Swamy; Kumar, M. Sunil

doi:10.1109/i-smac49090.2020.9243501

Cited by 51 publications

(18 citation statements)

References 12 publications

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“…A suitable technique for an ASL recognition system was put forth [7][33][45] [46]. They have previously assessed the symbols' effects, assembled different regional structures in the processed photos, and depending on the stated features, the signed touches were converted into text.…”

Section: Literature Surveymentioning

confidence: 99%

Real time sign language detection system using deep learning techniques

2022

pnr

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Humans require communication in order to survive. It is a fundamental and effective method for communicating thoughts, feelings, and points of view. A greater number of babies are being born with hearing abnormalities, which puts them at a communication disadvantage with the rest of the world, according to data on physically challenged children during the last decade. People who are deaf or hard of hearing typically use sign languages to communicate. Hand gestures are used by Deaf or Mute persons to communicate; as a result, non-Deaf people have a hard time understanding their messages. Systems that can detect different signs and provide information to common people are thus necessary. To address this issue, we have developed an automated sign language detection system using deep learning, which helps deaf/mute people can communicate with normal people.

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Section: Literature Surveymentioning

confidence: 99%

Real time sign language detection system using deep learning techniques

2022

pnr

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“…Additionally, most feature extractors are problem/task-oriented which means we cannot use an extractor trained on a certain domain for another. For instance, a feature extractor developed for detecting skin cancer [2] is not useful for detecting any other diseases.…”

Section: Motivationmentioning

confidence: 99%

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Shenavarmasouleh,

Mohammadi,

Amini

et al. 2021

Preprint

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Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical imaging. In this research, we propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find not only the type of lesions caused by DR, their corresponding bounding boxes, and their masks; but also the severity level of the overall case. Aside from traditional accuracy, we also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP). We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.

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“…Extracting features from skin cancer images is an important and arduous task. Automated computer diagnosis mechanism can improve accurate analysis of skin diseases, helping dermatologists shorten diagnosis time and improve patient care [37]. "In [38]," Deng et al CNN has established two branches to delete global and local structures to reach the burst limit.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesions Detection and Classification Using Deep Learning

2021

IJATCSE

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The skin lesion is considered the most widespread malignant disease in individuals, and melanoma is the deadliest form of the disease. Early detection affects the prognosis of the disease and improves the chances of survival. Dermatologists use scientific calculation tools such as ABCD to diagnose melanoma through visual inspection of the mole. However, computer vision tools have been presented to support the quantitative scrutiny of skin lesions. Significant improvements to deep learning algorithms in image recognition tasks should be very successful in medical image examination, particularly in the classification of skin lesions used to diagnose melanoma. In this research, a deep learning simulation with 38 layers to detect and classify skin lesions was proposed. Two datasets were used for training and testing i.e., the HAM10000 dataset & the ISIC2019 dataset. Experimental results show that the model outperforms on both the datasets hence making it nondependent of the dataset. 94.45% of validation top 3 accuracies are achieved on the HAM10000 dataset & 93.06% of validation top 3 accuracies are achieved on the ISIC2019 dataset.

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A Comparative Study of Melanoma Skin Cancer Detection in Traditional and Current Image Processing Techniques

Cited by 51 publications

References 12 publications

Real time sign language detection system using deep learning techniques

Real time sign language detection system using deep learning techniques

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Skin Lesions Detection and Classification Using Deep Learning

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