Automatic diagnosis of melanoma from dermoscopic image using real-time object detection

Roy, Shudipto Sekhar; Haque, Akkas Uddin; Neubert, Jeremiah

doi:10.1109/ciss.2018.8362245

Cited by 12 publications

(11 citation statements)

References 7 publications

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“…In the context of the types of data employed, Roy et al ( 28 ) and Alizadeh and Mahloojifar ( 29 ) used dermoscopic images from established databases like PH2 and ISIC, adding to the reliability of the results. Meanwhile, Dulmage et al ( 35 ) relies on clinical images collected by healthcare professionals, reflecting real-world conditions.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review

Furriel,

Oliveira,

Prôa

et al. 2024

Front. Med.

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BackgroundSkin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists. Its adoption requires analysis of efficacy, safety, and ethical considerations, as well as considering the genetic and ethnic diversity of patients.ObjectiveThe systematic review aims to examine research on the detection, classification, and assessment of skin cancer images in clinical settings.MethodsWe conducted a systematic literature search on PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis.ResultsThrough the search, 760 studies were identified in four databases, from which only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation.ConclusionThe application of artificial intelligence in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review

Furriel,

Oliveira,

Prôa

et al. 2024

Front. Med.

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“…Additionally, deep learning algorithms perform the classifications in a fraction of the time a human expert needs. There are already various applications of deep learning in medicine and biology, for example, melanoma detection (Roy et al, 2018), wildlife recognition and identification (Nguyen et al, 2017), EEG‐based screening of depression (Acharya et al, 2018) and genomic variant calling (Poplin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

CollembolAI, a macrophotography and computer vision workflow to digitize and characterize samples of soil invertebrate communities preserved in fluid

et al. 2022

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Soils are inhabited by communities of tiny invertebrates that participate in the essential functions of soils. Characterizing those communities in terms of species diversity and species abundance is part of investigating soil functions and response to perturbation. Dozens to hundreds of specimens can be extracted from a sample that need to be sorted, counted and identified. It involves an enormous amount of time, straining the workflow of soil zoologists. Deep learning‐based computer vision approaches have become increasingly popular to monitor biodiversity, as they can be applied to detect, count and classify organisms. In this work, we present CollembolAI, an open‐source prototype for a computer vision workflow. It includes a hardware system for acquiring high‐resolution pictures of soil species samples in fluid and a deep learning‐based application (Faster R‐CNN with Slicing Aided Hyper Inference) to train and evaluate models for the detection and classification of animals on those pictures. We evaluated the workflow using a mix of specimens belonging to 12 species of springtail and mite, picked from our taxonomic collection. Specimens were photographed multiple times under various angle of views. The model was trained using 5671 views on specimens on 30 images and tested on 442 views from new specimens on six images. CollembolAI is affordable, simple to build and allows the rapid digitization of mesofauna samples. Our deep learning model achieved a Precision of 0.940, Recall of 0.918 and a mAP@0.5 (Pascal VOC) of 0.868. The model showed a lower Recall for one species, but was performant on all others. Our prototype offers an operational workflow for the creation of soil fauna picture datasets needed to develop efficient deep learning‐based classifiers. The applications are numerous, for example, collection digitization, soil biodiversity analysis and monitoring, or automatic assessment of mesofauna‐based bioindicators. Computer vision is a rapidly emerging tool to handle efficiently the complexity of biodiversity and is already successfully used for plants and vertebrates recognition of pictures. It is on the way to become a major asset for dealing with invertebrate diversity. The code and setup instructions are available on Github.

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“…To reduce labor costs for manual extract image features of defects, the improved deep convolutional neural network is tested for defect detection [8]. Reference [9] just used a single neural network to the full image, enabling real-time performance. To improve the accuracy of defect inspection, reference [10] presented a new classification network, a multi-group convolutional neural network (MG-CNN), to extract the feature map groups of different types of defects.…”

Section: Introductionmentioning

confidence: 99%

Metal Surface Defect Detection Using Modified YOLO

Zhang

Wang

2021

Algorithms

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Aiming at the problems of inefficient detection caused by traditional manual inspection and unclear features in metal surface defect detection, an improved metal surface defect detection technology based on the You Only Look Once (YOLO) model is presented. The shallow features of the 11th layer in the Darknet-53 are combined with the deep features of the neural network to generate a new scale feature layer using the basis of the network structure of YOLOv3. Its goal is to extract more features of small defects. Furthermore, then, K-Means++ is used to reduce the sensitivity to the initial cluster center when analyzing the size information of the anchor box. The optimal anchor box is selected to make the positioning more accurate. The performance of the modified metal surface defect detection technology is compared with other detection methods on the Tianchi dataset. The results show that the average detection accuracy of the modified YOLO model is 75.1%, which ia higher than that of YOLOv3. Furthermore, it also has a great detection speed advantage, compared with faster region-based convolutional neural network (Faster R-CNN) and other detection algorithms. The improved YOLO model can make the highly accurate location information of the small defect target and has strong real-time performance.

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Automatic diagnosis of melanoma from dermoscopic image using real-time object detection

Cited by 12 publications

References 7 publications

Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review

Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review

CollembolAI, a macrophotography and computer vision workflow to digitize and characterize samples of soil invertebrate communities preserved in fluid

Metal Surface Defect Detection Using Modified YOLO

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