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

https://doi.org/10.1101/382010

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/archderm.131.3.286openalex-snapshot

Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1038/sdata.2018.161openalex-snapshot

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

inferredcites →https://doi.org/10.1109/cvpr.2006.153openalex-snapshot

Incremental learning of object detectors using a visual shape alphabet

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00137openalex-snapshot

Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria

inferredcites →https://doi.org/10.1109/icicict46008.2019.8993219openalex-snapshot

Skin Lesion Analysis Towards Melanoma Detection

inferredcites →https://doi.org/10.1109/tmi.2016.2642839openalex-snapshot

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

inferredcites →https://doi.org/10.3390/s18020556openalex-snapshot

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/ima.22776openalex-snapshot

HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

inferred← citeshttps://doi.org/10.1007/978-3-031-86299-1_1openalex-snapshot

Deep Learning Methodologies for Segmentation and Classification of Cutaneous Malignancies Utilizing Capsule Networks

inferred← citeshttps://doi.org/10.1007/978-3-032-15614-3_2openalex-snapshot

Dermatology: The Ideal Field for A.I. to Thrive

inferred← citeshttps://doi.org/10.1007/978-981-15-1918-5_5openalex-snapshot

Color Models for Skin Lesion Classification from Dermatoscopic Images

inferred← citeshttps://doi.org/10.1007/978-981-15-6321-8openalex-snapshot

Deep Learning for Cancer Diagnosis

inferred← citeshttps://doi.org/10.1007/978-981-15-6321-8_5openalex-snapshot

Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma

inferred← citeshttps://doi.org/10.1007/978-981-33-6966-5_13openalex-snapshot

The Effect of Different Feature Selection Methods for Classification of Melanoma

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.104729openalex-snapshot

Detection of melanoma with hybrid learning method by removing hair from dermoscopic images using image processing techniques and wavelet transform

inferred← citeshttps://doi.org/10.1016/j.cie.2022.108060openalex-snapshot

Detection of melanoma in dermoscopic images by integrating features extracted using handcrafted and deep learning models

inferred← citeshttps://doi.org/10.1016/j.cmpb.2020.105351openalex-snapshot

Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106333openalex-snapshot

2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.106624openalex-snapshot

A survey, review, and future trends of skin lesion segmentation and classification

inferred← citeshttps://doi.org/10.1038/s41598-023-49721-xopenalex-snapshot

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework

inferred← citeshttps://doi.org/10.1109/asiancon62057.2024.10837986openalex-snapshot

A Convolution Neural Network Based Classifier for Discerning AI Generated Images and Real Images

inferred← citeshttps://doi.org/10.1109/bia48344.2019.8967469openalex-snapshot

Pigmented Skin Lesions Classification using Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1109/bia50171.2020.9244493openalex-snapshot

Melanoma Detection from Dermatoscopic Images using Deep Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1109/ehb55594.2022.9991668openalex-snapshot

Melanoma Detection using a Random Forest Algorithm

inferred← citeshttps://doi.org/10.1109/gcat52182.2021.9587838openalex-snapshot

Malignant Melenoma and Atypical Nevus Classification Using Machine Learning with Shape and Assymetric Features

inferred← citeshttps://doi.org/10.1109/iccakm46823.2020.9051495openalex-snapshot

Detection of Melanoma using Deep Learning Techniques

inferred← citeshttps://doi.org/10.1109/icicis58388.2023.10391178openalex-snapshot

Deep Learning Models for Skin Cancer Detection: A review

inferred← citeshttps://doi.org/10.1109/iisa.2019.8900769openalex-snapshot

Pigmented Skin Lesions Classification Using Data Driven Subsets of Image Features

inferred← citeshttps://doi.org/10.1109/ocit53463.2021.00048openalex-snapshot

SkinAid: A GAN-based Automatic Skin Lesion Monitoring Method for IoMT Frameworks

inferred← citeshttps://doi.org/10.1111/srt.12794openalex-snapshot

Automated grading of acne vulgaris by deep learning with convolutional neural networks

inferred← citeshttps://doi.org/10.1371/journal.pone.0322659openalex-snapshot

RETRACTED: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning

inferred← citeshttps://doi.org/10.22463/17948231.3286openalex-snapshot

Preliminary Identification of Skin Lesions using Efficient Computational Learning Techniques

inferred← citeshttps://doi.org/10.30534/ijatcse/2019/1581.62019openalex-snapshot

An Improved Framework by Mapping Salient Features for Skin Lesion Detection and Classification using the Optimized Hybrid Features

inferred← citeshttps://doi.org/10.33564/ijeast.2021.v06i08.012openalex-snapshot

CLASSIFICATION OF MELANOMA AND NEVUS IN SKIN CANCER LESIONS

inferred← citeshttps://doi.org/10.3390/app14031084openalex-snapshot

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

inferred← citeshttps://doi.org/10.3390/cancers13215256openalex-snapshot

Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques

inferred← citeshttps://doi.org/10.3390/cancers17010137openalex-snapshot

AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data

inferred← citeshttps://doi.org/10.3390/diagnostics12071747openalex-snapshot

Melanoma Detection Using XGB Classifier Combined with Feature Extraction and K-Means SMOTE Techniques

inferred← citeshttps://doi.org/10.3390/e22040484openalex-snapshot

Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures

inferred← citeshttps://doi.org/10.3390/math12071030openalex-snapshot

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

inferred← citeshttps://doi.org/10.5120/ijca2022922504openalex-snapshot

Recognizing Pigmented Skin Lesion based on LightGBM Classifier and Deep Saliency Segmentation using Convolutional Neural Network

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