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Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images [article]

Noel Codella, Quoc-Bao Nguyen, Sharath Pankanti, David Gutman, Brian Helba, Allan Halpern, John R. Smith
2016 arXiv   pre-print
Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin  ...  lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection.  ...  and insightful discussions in dermoscopy and dermatology.  ... 
arXiv:1610.04662v2 fatcat:pvpoyzejlndr5fdgrsknkblaja

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

Ahmad Naeem, Muhammad Shoaib Farooq, Adel Khelifi, Adnan Abid
2020 IEEE Access  
[47] Ensemble of Deep Convolutional Neural Network for Skin Lesion Classification Dermoscopy CNN Ensemble CNN PH2 [38] [48] Detection of Malignant Melanomas in Dermoscopic Images by  ...  cancer using GDA for CNN training Dermoscopy DCNN Gradient descent Algorithm(GDA) ISIC 2016 [5] [25] Automated Melanoma Recognition using FCN Dermoscopy Hybrid (FCN+CNN) Deep Residual  ... 
doi:10.1109/access.2020.3001507 fatcat:hmlampsx3zetjphzqg4brun3xm

Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations [article]

Xiaoxiao Li, Junyan Wu, Hongda Jiang, Eric Z. Chen, Xu Dong, Ruichen Rong
2018 bioRxiv   pre-print
Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of skin lesion is extremely challenging manually visualization.  ...  In this paper, we proposed a two-stage method to combine deep learning features and clinical criteria representations to address skin lesion automated diagnosis task.  ...  In stage 1, we generated image features through deep learning and traditional handcrafted approaches.  ... 
doi:10.1101/382010 fatcat:d2ypwci7ivbf5ey5vhszoblrai

What evidence does deep learning model use to classify Skin Lesions? [article]

Xiaoxiao Li, Junyan Wu, Eric Z. Chen, Hongda Jiang
2019 arXiv   pre-print
Recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging based decision making.  ...  Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate.  ...  Deep Learning Image Classifier CNNs have led to breakthroughs in natural images classification and object recognition.  ... 
arXiv:1811.01051v3 fatcat:rg2lhdxo2ngi7gtmjpevc7q6wu


Vanaja C, Pragadeesh M, Rathesh R
Dermoscopy is a non-invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin.  ...  Melanoma is a type of cancer that mostly starts in pigment cells (melanocytes) in the skin. To improve the diagnostic performance of melanoma, a dermoscopy technique was developed.  ...  Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network In this paper, Lisheng Wei., et al.  ... 
doi:10.17148/ijireeice.2022.10229 fatcat:ffirznddirbptgh2ib2dg3p6ru

Haar Hybrid Transform Based Melanoma Identification Using Ensemble of Machine Learning Algorithms

Sudeep D Thepade, Gaurav Ramnani, Shubham Mandhare
2020 ELCVIA Electronic Letters on Computer Vision and Image Analysis  
Experimentation performed on the transformed dermoscopy skin images with machine learning algorithms and their ensembles gives rise to a total of 196 variations.  ...  This paper explores hybrid wavelet transform based melanoma identification using ensemble of machine learning algorithms.  ...  and the ensembles of multiple machine learning algorithms using percentage specificity of melanoma skin cancer identification of dermoscopy images in the dataset, keeping the fractional coefficient 32x32  ... 
doi:10.5565/rev/elcvia.1236 fatcat:zqpgrzipqnbfngvicj2gw5e6x4

Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy

Michael Phillips, Jack Greenhalgh, Helen Marsden, Ioulios Palamaras
2019 Dermatology Practical & Conceptual  
This study aimed to evaluate the accuracy of an artificial intelligence neural network (Deep Ensemble for Recognition of Melanoma [DERM]) to identify malignant melanoma from dermoscopic images of pigmented  ...  Malignant melanoma can most successfully be cured when diagnosed at an early stage in the natural history.  ...  Ensemble for Recognition of Melanoma (DERM) algorithm by lesion type.  ... 
doi:10.5826/dpc.1001a11 pmid:31921498 pmcid:PMC6936633 fatcat:hqppm45m35dnndgkeeixupabhm

Dermoscopy image classification based on StyleGANs and decision fusion

An Gong, Xinjie Yao, Wei Lin
2020 IEEE Access  
Our proposed method can improve the accuracy of dermoscopy image classification and provide help for dermatologists.  ...  At present, dermoscopy is an effective way for the early diagnosis of skin cancer.  ...  Yu et al. proposed a novel method based on very deep CNNs to meet the challenges of automated melanoma recognition in dermoscopy images, which consists of two steps: segmentation and classification [39  ... 
doi:10.1109/access.2020.2986916 fatcat:xxc2r6vifbh3bgtj7qnfvpcali

Artificial Intelligence in Cutaneous Oncology

Yu Seong Chu, Hong Gi An, Byung Ho Oh, Sejung Yang
2020 Frontiers in Medicine  
In addition, the universal use of dermoscopy, which allows for non-invasive inspection of the upper dermal level of skin lesions with a usual 10-fold magnification, adds to the image storage and analysis  ...  Skin cancer, previously known to be a common disease in Western countries, is becoming more common in Asian countries. Skin cancer differs from other carcinomas in that it is visible to our eyes.  ...  In this figure, the examples for conventional machine learning and deep learning are classifications of acral lentiginous melanoma (ALM) and benign nevus (BN) in dermoscopy images.  ... 
doi:10.3389/fmed.2020.00318 pmid:32754606 pmcid:PMC7366843 fatcat:557xllh2dzf5hbnrrmcdbg2kyi

Using Transfer Learning and Hierarchical Classifier to Diagnose Melanoma From Dermoscopic Images

Priti Bansal, Sumit Kumar, Ritesh Srivastava, Saksham Agarwal
2021 International Journal of Healthcare Information Systems and Informatics  
Many intelligent systems based on image processing and machine learning have been proposed by researchers in the past to detect different kinds of diseases that are successfully used by healthcare organisations  ...  In this paper, a model is proposed in which features are extracted using convolutional neural network (CNN) with transfer learning and a hierarchical classifier consisting of random forest (RF), k-nearest  ...  The deep learning networks are first trained using the images in the training set.  ... 
doi:10.4018/ijhisi.20210401.oa4 fatcat:gu4a5llgx5a45m6ywui5lwxjre

Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)

Seeja R D, Suresh A
2019 Asian Pacific Journal of Cancer Prevention  
Conclusion: In deep learning environment, U-Net segmentation algorithm is found to be the best method for segmentation and it helps to improve the classification performance.  ...  It can be assist medical experts on early diagnosis of melanoma on dermoscopy images. Methods: First A Convolutional Neural Network (CNN) based U-net algorithm is used for segmentation process.  ...  This is because unsegment image size is very large and artifacts in images. Deep learning based segmented images can generate more discriminate features for better recognition.  ... 
doi:10.31557/apjcp.2019.20.5.1555 pmid:31128062 pmcid:PMC6857898 fatcat:ax3ukxgldrefjla7lsoqn6ec3e

Melanoma Segmentation and Classification using Deep Learning

At the same time accurate diagnosis is very essential because of the similarities of melanoma and benign lesions. Hence computerized recognition approaches are highly demanded for dermoscopy images.  ...  Based on these values the deep learning based classification with segmented images produces better result and it helps to improve the diagnosis performance.  ...  This is because unsegment image size is very large and artifacts in images. Deep Learning based segmented images can generate more discriminate features for better recognition.  ... 
doi:10.35940/ijitee.l2516.1081219 fatcat:gjmzpumgt5dzjml7xqitlk7xuq

Dermoscopic Image Classification Method Using an Ensemble of Fine-Tuned Convolutional Neural Networks

Xin Shen, Lisheng Wei, Shaoyu Tang
2022 Sensors  
Aiming at the problems of large intra-class differences, small inter-class differences, low contrast, and small and unbalanced datasets in dermoscopic images, this paper proposes a dermoscopic image classification  ...  ISIC 2016 Challenge official skin dataset, we integrated the outputs of the three base models using a weighted fusion ensemble strategy in order to obtain a final prediction result able to distinguish  ...  Dermatology and Venereal Diseases at The First Affiliated Hospital of Wannan Medical College, for his professional guidance on this paper.  ... 
doi:10.3390/s22114147 pmid:35684768 pmcid:PMC9185225 fatcat:ow3vi5d6cbhitnmgrc2pjqlzce

Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods [article]

Manu Goyal and Amanda Oakley and Priyanka Bansal and Darren Dancey and Moi Hoon Yap
2019 arXiv   pre-print
In this work, we propose the use of fully automated deep learning ensemble methods for accurate lesion boundary segmentation in dermoscopic images.  ...  Furthermore, the proposed ensemble method achieved an accuracy of 95.6% for some representative clinically benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases on  ...  However, recent advancement in computer vision algorithms especially with latest deep learning methods, the image recognition has vastly improved in few years.  ... 
arXiv:1902.00809v2 fatcat:dygddnqeanczhmauifsif6bt5y

Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions

Qaisar Abbas, Misbah Sadaf, Anum Akram
2016 Computers  
The overall system is evaluated using a dataset of 350 dermoscopy images (50 ROIs per class).  ...  The optimal color and texture features are also extracted from each region-of-interest (ROI) dermoscopy image and then these normalized features are fed into an MV-SVM classifier to recognize seven classes  ...  Likewise, in [24] , the authors have presented a different model for recognition of melanoma based on advanced bag-of-visual-words and deep learning algorithms.  ... 
doi:10.3390/computers5030013 fatcat:fhu7ebimazgsvgwwh6du5pkfte
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