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

Manzo, Mario; Pellino, Simone

doi:10.3390/jimaging6120129

Cited by 25 publications

(15 citation statements)

References 42 publications

(37 reference statements)

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“…Compared with the previous studies, [20][21][22][23][24][25] our result has a similar high performance even though we used less dataset, unbalanced data which was closer to the clinical setting…”

Section: Discussionsupporting

confidence: 52%

Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection

Ningrum¹,

Yuan²,

Kung³

et al. 2021

JMDH

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Background: Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin lesions and limited standard dataset availability. Recent research indicates the potential of deep convolutional neural networks (CNN) in predicting outcomes from simple as well as highly complicated images. However, its implementation requires high-class computational facility, that is not feasible in low resource and remote areas of health care. There is potential in combining image and patient's metadata, but the study is still lacking. Objective: We want to develop malignant melanoma detection based on dermoscopic images and patient's metadata using an artificial intelligence (AI) model that will work on low-resource devices. Methods:We used an open-access dermatology repository of International Skin Imaging Collaboration (ISIC) Archive dataset consist of 23,801 biopsy-proven dermoscopic images. We tested performance for binary classification malignant melanomas vs nonmalignant melanomas. From 1200 sample images, we split the data for training (72%), validation (18%), and testing (10%). We compared CNN with image data only (CNN model) vs CNN for image data combined with an artificial neural network (ANN) for patient's metadata (CNN+ANN model). Results: The balanced accuracy for CNN+ANN model was higher (92.34%) than the CNN model (73.69%). Combination of the patient's metadata using ANN prevents the overfitting that occurs in the CNN model using dermoscopic images only. This small size (24 MB) of this model made it possible to run on a medium class computer without the need of cloud computing, suitable for deployment on devices with limited resources. Conclusion:The CNN+ANN model can increase the accuracy of classification in malignant melanoma detection even with limited data and is promising for development as a screening device in remote and low resources health care.

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Section: Discussionsupporting

confidence: 52%

Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection

Ningrum¹,

Yuan²,

Kung³

et al. 2021

JMDH

View full text Add to dashboard Cite

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“…This methodology was used in this work due to its excellent performance in solving several other classification problems, as introduced by [ 44 , 45 , 46 , 47 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

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In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.

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“…Thus, ECLAD-Net can be utilized for both of these purposes with slight modifications, as they are special cases of PCB component detection. Similarly, this is also applicable to problems in the science and medical spheres for the detection of small components in medical imaging, such as in [43].…”

Section: Edge Casesmentioning

confidence: 99%

Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

et al. 2021

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In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.

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

Cited by 25 publications

References 42 publications

Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection

Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

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