Melanoma detection from dermoscopy images using Nasnet Mobile with Transfer Learning

Çakmak, Mustafa; Tenekeci, Mehmet Emin

doi:10.1109/siu53274.2021.9477985

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…It is 29 MB in size without its top layers. NASNet-Mobile : This is another lightweight CNN used for classification problems [ 36 ]. This model is 23 MB without its top layers.…”

Section: Methodsmentioning

confidence: 99%

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Silva

Luz

et al. 2023

Sensors

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Deep Learning models have presented promising results when applied to Agriculture 4.0. Among other applications, these models can be used in disease detection and fruit counting. Deep Learning models usually have many layers in the architecture and millions of parameters. This aspect hinders the use of Deep Learning on mobile devices as they require a large amount of processing power for inference. In addition, the lack of high-quality Internet connectivity in the field impedes the usage of cloud computing, pushing the processing towards edge devices. This work describes the proposal of an edge AI application to detect and map diseases in citrus orchards. The proposed system has low computational demand, enabling the use of low-footprint models for both detection and classification tasks. We initially compared AI algorithms to detect fruits on trees. Specifically, we analyzed and compared YOLO and Faster R-CNN. Then, we studied lean AI models to perform the classification task. In this context, we tested and compared the performance of MobileNetV2, EfficientNetV2-B0, and NASNet-Mobile. In the detection task, YOLO and Faster R-CNN had similar AI performance metrics, but YOLO was significantly faster. In the image classification task, MobileNetMobileV2 and EfficientNetV2-B0 obtained an accuracy of 100%, while NASNet-Mobile had a 98% performance. As for the timing performance, MobileNetV2 and EfficientNetV2-B0 were the best candidates, while NASNet-Mobile was significantly worse. Furthermore, MobileNetV2 had a 10% better performance than EfficientNetV2-B0. Finally, we provide a method to evaluate the results from these algorithms towards describing the disease spread using statistical parametric models and a genetic algorithm to perform the parameters’ regression. With these results, we validated the proposed pipeline, enabling the usage of adequate AI models to develop a mobile edge AI solution.

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“…It is 29 MB in size without its top layers. NASNet-Mobile : This is another lightweight CNN used for classification problems [ 36 ]. This model is 23 MB without its top layers.…”

Section: Methodsmentioning

confidence: 99%

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Silva

Luz

et al. 2023

Sensors

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“…A single-modality large-scale dataset, including a total of 64699 images, was used to calculate the performance of the CAD method. Similarly, Jasil et al [16] and Çakmak et al [17] utilized different CNNs for skin lesion classification tasks. In [16], a pretrained CNN, named DenseNet201 [28], was employed to classify dermoscopy images into one of seven different classes of skin lesions.…”

Section: A Image-based Methods (2d Models)mentioning

confidence: 99%

“…A single-modality limited dataset, including a total of 3091 images, was used to validate the method. Later, Çakmak et al [17] used a lightweight CNN, named NASNetMobile [29], for melanoma detection from dermoscopy images. They considered a larger dataset (including a total of 10015 images) than that of [16].…”

Section: A Image-based Methods (2d Models)mentioning

confidence: 99%

Volumetric Model Genesis in Medical Domain for the Analysis of Multimodality 2-D/3-D Data Based on the Aggregation of Multilevel Features

Owais

Cho

Park

2023

IEEE Trans. Ind. Inf.

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The automatic and accurate classification of medical imaging data has potential applications in computer-aided disease diagnosis, prognosis, and treatment. However, it remains a challenge to optimize recent deep learning algorithms in medical domain for the accurate classification of large-scale 3D volumetric data. To address these challenges, we propose an efficient deep volumetric classification network based on the aggregation of multilevel deep features for accurate classification of large-scale medical 2D/3D imaging data. To perform a detailed quantitative analysis of our method, 26 different datasets were fused to construct a single large-scale multimodal database that comprises a total of seventy different classes, including 151,095 data samples. Additionally, 15 different baseline methods were configured under the same experimental protocol for volumetric model genesis and extensive performance comparison with our method. The experimental results of our method exhibited promising performance as area under the curve of 93.66% and outperformed various state-of-the-art methods.

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“…In this study, the suggested methodology yielded an adequate precision of 78.0%. Using the HAM10000 [3] unbalanced dataset, the melanoma detection model created by Cakmak and Tenekeci [100] with a Basnet mobile neural network attained an accuracy of 89.20% in 2021. Furthermore, 97.90% accuracy in the identification of skin cancer was attained with the dataset.…”

Section: Cnn In the Detection Of Skin Cancermentioning

confidence: 99%

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Hussain,

Toscano

2024

Symmetry

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Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin cancer has a higher fatality rate, and it expands gradually to different areas of the body. The rapid growth of automated diagnosis frameworks has led to the combination of diverse machine learning, deep learning, and computer vision algorithms for detecting clinical samples and atypical skin lesion specimens. Automated methods for recognizing skin cancer that use deep learning techniques are discussed in this article: convolutional neural networks, and, in general, artificial neural networks. The recognition of symmetries is a key point in dealing with the skin cancer image datasets; hence, in developing the appropriate architecture of neural networks, as it can improve the performance and release capacities of the network. The current study emphasizes the need for an automated method to identify skin lesions to reduce the amount of time and effort required for the diagnostic process, as well as the novel aspect of using algorithms based on deep learning for skin lesion detection. The analysis concludes with underlying research directions for the future, which will assist in better addressing the difficulties encountered in human skin cancer recognition. By highlighting the drawbacks and advantages of prior techniques, the authors hope to establish a standard for future analysis in the domain of human skin lesion diagnostics.

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Melanoma detection from dermoscopy images using Nasnet Mobile with Transfer Learning

Cited by 13 publications

References 16 publications

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Volumetric Model Genesis in Medical Domain for the Analysis of Multimodality 2-D/3-D Data Based on the Aggregation of Multilevel Features

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

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