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Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks

Khalil Aljohani, Turki Turki
2022 AI  
Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma.  ...  Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience.  ...  Unlike ResNet50 (consisting of 49 convolutional layers, 1 max-pooling, 1 average pooling layer, and 1 fully connected layer for classification) [43] , ResNet50V2 is a modified version using a different  ... 
doi:10.3390/ai3020029 fatcat:zzn2xvwf2fa2hoa5boxrwioz4i

Skin Lesions Classification and Segmentation: A Review

Marzuraikah Mohd Stofa, Mohd Asyraf Zulkifley, Muhammad Ammirrul Atiqi Mohd Zainuri
2021 International Journal of Advanced Computer Science and Applications  
An automated intelligent system based on imaging input for unbiased diagnosis of skin-related diseases is an essential screening tool nowadays.  ...  This is because visual and manual analysis of skin lesion conditions based on images is a time-consuming process that puts a significant workload on health practitioners.  ...  ACKNOWLEDGMENT The authors would like to acknowledge funding from Universiti Kebangsaan Malaysia (Geran Universiti Penyelidikan: GUP-2019-008) and Ministry of Higher Education Malaysia (Fundamental Research  ... 
doi:10.14569/ijacsa.2021.0121060 fatcat:xvliew62nvc6ldbq7z3f2nauta

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

Dan Popescu, Mohamed El-Khatib, Hassan El-Khatib, Loretta Ichim
2022 Sensors  
The paper presents a systematic review of recent advances in an area of increased interest for cancer prediction, with a focus on a comparative perspective of melanoma detection using artificial intelligence  ...  This is the reason why many researchers in this domain wanted to obtain accurate computer-aided diagnosis systems to assist in the early detection and diagnosis of such diseases.  ...  Recently, a series of published SL diagnosis systems used newer versions of GoogLeNet.  ... 
doi:10.3390/s22020496 pmid:35062458 pmcid:PMC8778535 fatcat:ccxnttxunngt3jq4mdegofa64a

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance Than Deep Neural Networks

Liyang Wang, Angxuan Chen, Yan Zhang, Xiaoya Wang, Yu Zhang, Qun Shen, Yong Xue
2020 Diagnostics  
An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK.  ...  At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment.  ...  In view of the situation above, it is of great significance to develop a portable app for accurate automatic diagnosis of AK.  ... 
doi:10.3390/diagnostics10040217 pmid:32294962 pmcid:PMC7235884 fatcat:ldqitksbvndwllni4fgc7llfkq

Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions

Hassan El-Khatib, Dan Popescu, Loretta Ichim
2020 Sensors  
First, we created a neural network (NN) that can differentiate melanoma from benign nevus. The NN architecture is analyzed by evaluating it during the training process.  ...  A comparison of the obtained results from all the methods is then done.  ...  Figure 20 . 20 (a) Confusion matrix for validation phase in the case of GoogleNet pre-trained on Places365 for the PH 2 database; (b) Confusion matrix for validation phase in the case of GoogleNet pre-trained  ... 
doi:10.3390/s20061753 pmid:32245258 fatcat:463n5jmyifatbi2ytny7hngche

Accuracy of Deep Neural Network in Triaging Common Skin Diseases of Primary Care Attention

Mara Giavina-Bianchi, Eduardo Cordioli, André P Dos Santos
2021 Frontiers in Medicine  
89.72% for diagnosis, 96.03% for referrals and 92.54% for priority level in 6,975 image testing.  ...  The most accurate algorithm was then tested for accuracy in diagnosis, referral, and level of priority given to 6,945 cases.  ...  Currently, a large number of articles show the development of algorithms for diagnostic support of diseases such as melanoma or a group of disorders such as skin cancer mainly using dermoscopic images  ... 
doi:10.3389/fmed.2021.670300 pmid:34513863 pmcid:PMC8427035 fatcat:hqsaqf7ibfhvbepk6zk6fkcqmi

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Mohamed A. Kassem, Khalid M. Hosny, Robertas Damaševičius, Mohamed Meselhy Eltoukhy
2021 Diagnostics  
Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems.  ...  This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/diagnostics11081390 fatcat:r4gyqfwberfofhcbx2xsn7vpf4

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

Mario Manzo, Simone Pellino
2020 Journal of Imaging  
Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate.  ...  Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation.  ...  He followed us during the first steps towards the Computer Science, through a whirlwind of goals, ideas and, especially, love and passion for the work. We will be forever grateful great master.  ... 
doi:10.3390/jimaging6120129 pmid:34460526 pmcid:PMC8321205 fatcat:xuzusoeiozbn5mrkjjmtknwu24

Representation Learning on Large and Small Data [article]

Chun-Nan Chou, Chuen-Kai Shie, Fu-Chieh Chang, Jocelyn Chang, Edward Y. Chang
2017 arXiv   pre-print
Transfer representation learning gave the OM and melanoma diagnosis modules of our XPRIZE Tricorder device (which finished 2^nd out of 310 competing teams) a significant boost in diagnosis accuracy.  ...  In terms of big data, it has been widely accepted in the research community that the more data the better for both representation and classification improvement.  ...  For an introduction to the computation of neural network models, please refer to [17] .  ... 
arXiv:1707.09873v1 fatcat:lhrqlkdfcrfgtn6rluyotvyn4u

Large-Scale Social Multimedia Analysis [chapter]

Benjamin Bischke, Damian Borth, Andreas Dengel
2019 Big Data Analytics for Large-Scale Multimedia Search  
For example, the features of an image can be affected by the other images in the CNN (because the structure parameters modified through back-propagation are affected by all training images), but the feature  ...  for object detection and image classification on a subset of ImageNet, 1.2 million images over 1000 categories.  ...  For an introduction to the computation of neural network models, refer to [71] .  ... 
doi:10.1002/9781119376996.ch6 fatcat:dw4rzuqeanbvxmaabtsgrid2ty

Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks

Rehan Raza, Fatima Zulfiqar, Shehroz Tariq, Gull Bano Anwar, Allah Bux Sargano, Zulfiqar Habib
2021 Mathematics  
Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer.  ...  In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried out.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math10010026 fatcat:wrquxgz5und6lobcr7tx5ktsya

A Comprehensive Evaluation and Benchmarking of Convolutional Neural Networks for Melanoma Diagnosis

Saeed Alzahrani, Baidaa Al-Bander, Waleed Al-Nuaimy
2021 Cancers  
Melanoma diagnosis is difficult, even for experienced dermatologists, due to the wide range of morphologies in skin lesions.  ...  Given the rapid development of deep learning algorithms for melanoma diagnosis, it is crucial to validate and benchmark these models, which is the main challenge of this work.  ...  This research direction aims to conduct a comprehensive evaluation and benchmark of convolutional neural networks for melanoma diagnosis.  ... 
doi:10.3390/cancers13174494 pmid:34503300 fatcat:a4dlzdf4bbh5bokcdac4k65qzq

Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

Arthur Cartel Foahom Gouabou, Jean-Luc Damoiseaux, Jilliana Monnier, Rabah Iguernaissi, Abdellatif Moudafi, Djamal Merad
2021 Sensors  
Our results reveal that the directed acyclic graph is a meaningful approach to develop a reliable and robust automated diagnosis system for the multiclass classification of dermoscopic images.  ...  Unlike conventional ensemble methods, we use a directed acyclic graph to aggregate binary CNNs for the melanoma detection task.  ...  A total of 9.6 suspicious benign lesions are excised before reaching a confirmed diagnosis of melanoma [7] . Each excision can lead to scarring and post-surgery complications.  ... 
doi:10.3390/s21123999 fatcat:fbxbezllvzapfavv2f7w4vpnku

Artificial Intelligence in Cutaneous Oncology

Yu Seong Chu, Hong Gi An, Byung Ho Oh, Sejung Yang
2020 Frontiers in Medicine  
Although skin biopsy is essential for the diagnosis of skin cancer, decisions regarding whether or not to conduct a biopsy are made by an experienced dermatologist.  ...  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  ...  CANCERS Melanoma A total of 18 publications were identified, six of these described the use of conventional machine learning, nine publications FIGURE 3 | Example of DCNN for classifying ALM and BN in  ... 
doi:10.3389/fmed.2020.00318 pmid:32754606 pmcid:PMC7366843 fatcat:557xllh2dzf5hbnrrmcdbg2kyi

A scoping review of transfer learning research on medical image analysis using ImageNet [article]

Mohammad Amin Morid, Alireza Borjali, Guilherme Del Fiol
2020 arXiv   pre-print
We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome.  ...  AlexNet was the most frequent model for brain MRIs (36%) and breast X-Rays (50%). 35% of the studies compared their model with other well-trained CNN models and 33% of them provided visualization for interpretation  ...  Xception Xception stands for extreme inception and is a modified version of the Inception-V3 [25] .  ... 
arXiv:2004.13175v5 fatcat:wqghyqq4wfgpnpatvftty4vzx4
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