Development of Mobile Skin Cancer Detection using Faster R-CNN and MobileNet v2 Model

Hartanto, Cahyo Adhi; Wibowo, Adi

doi:10.1109/icitacee50144.2020.9239197

Cited by 33 publications

(13 citation statements)

References 15 publications

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“…So, its optimization mainly focuses on improving the performance of these two targets. For example, R-CNN model [14] was used by author to detect Melanoma. Quicker R-CNN has 3 main parts that are convolutional layers for feature extraction process.…”

Section: Optimization Based On Traditional Architecturementioning

confidence: 99%

“…Comparing to traditional CNN model, Faster R-CNN can provide a strong computing capability, largely shortening training time. In the experiment executed by authors of [14] There are other improvements made by acknowledging the components in CNN. [15] states a new method for feature extraction.…”

Section: Optimization Based On Traditional Architecturementioning

confidence: 99%

“…MobileNet-V2 is improved based on MobileNet-V1, which solves the problem of nonlinearities in the narrow layers of the model. This progress is made by reducing parameters in Depthwise Separable Convolution [16]. MobileNet-V3 imports NAS (Neural architectural search) to construct its based model MnasNet, and a new activation function h-swish(x) is used [17].…”

Section: Light Weight Cnnmentioning

confidence: 99%

See 2 more Smart Citations

Convolution Neural Network Method for Skin Cancer Diagnosis: Comparison and Improvement

Li¹,

Pang²,

Meng³

et al. 2022

HSET

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Skin cancer, the most common cancer in the world, has many detection steps and the detection process is easy to make mistakes. A detection method based on convolutional neural network (CNN) is proposed to assist doctors in the detection. Based on the development of CNN in the classification and diagnosis of skin cancer in recent years, this paper compares and summarizes the development of each step in this process. After reviewing previous papers, it can be concluded that the classification process is roughly divided into four parts. In addition, the evaluation indicators of the model are further analyzed. AUC Sen and SPE are the most basic evaluation indicators in the model evaluation. As a skin classifier, CNN improves the accuracy of classification and diagnosis results to a great extent. CNN model has also made progress in "lightweight" and "concise". However, there are few evaluation indicators available for different CNN methods, and the evaluation latitude is relatively single. In the future, the evaluation indicators should develop in more aspects, it will enable to better understand the personality of a CNN model.

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Section: Optimization Based On Traditional Architecturementioning

confidence: 99%

Section: Optimization Based On Traditional Architecturementioning

confidence: 99%

Section: Light Weight Cnnmentioning

confidence: 99%

See 1 more Smart Citation

Convolution Neural Network Method for Skin Cancer Diagnosis: Comparison and Improvement

Li¹,

Pang²,

Meng³

et al. 2022

HSET

View full text Add to dashboard Cite

show abstract

“…CNNs had also been utilized to develop mammogram-based breast cancer detection systems [11,12]. Regarding skin cancer, Researches have used CNNs to classify and extract the lesion boundary from this type of cancer [13,14]. CNN can implicitly extract the ABCDE rule, which is commonly used to identify skin cancers, which is asymmetry of the two halves of the skin lesion, smoothness of the border, color contrast, and lesion diameter size [15].…”

Section: Introductionmentioning

confidence: 99%

MSDSC: A Multistage Deep Learning-based Skin Cancer Classifier

Hassan¹,

Mahar²,

Fouad³

2023

Preprint

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One of the most frequent types of cancer in the globe is skin cancer which is a complicated public health issue. A sample of tissue from the skin lesion confirms the diagnosis of skin cancer. However, before making a definitive diagnosis, professionals detect specific signs that can be considered as early diagnosis. An early skin cancer diagnosis is prone to mistakes due to specialists’ lack of knowledge and similarities with other illnesses. This study presents a multistage deep learning-based skin cancer classifier (MSDSC) to give early detection of melanoma and non-melanoma skin lesions. The first stage is the pre-processing of samples to remove the hair surrounding the skin lesion. Since the dataset is unbalanced, the second stage was creating synthetic images using the Generative Adversarial Networks (GAN) model. This stage is followed by an attention-based U-Net model that gener- ates masks for regions of interest to eliminate the background. Lastly, two different types of EfficientNet are trained to classify skin lesions using segmented images. The proposed framework was tested using the International Skin Imaging Collaboration dataset (ISIC) and the results showed that it outperformed the state-of-the-art studies achieving an accuracy of 0.96, F1-score of 0.91, recall of 0.95, and precision of 0.88.

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“…Though, a significant number of cases remain unobserved until it reach to advanced stages, which reduces the chances of survival. An appealing method for early recognition is to employ automated classification of dermoscopic images analysed via Computer Based Diagnosis (CBD) system [5,6]. CBD is basically clinical decision support system that assists clinicians in the understanding of medical images.…”

Section: Introductionmentioning

confidence: 99%

Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network

et al. 2022

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Skin cancer is caused due to unusual development of skin cells and deadly type cancer. Early diagnosis is very significant and can avoid some categories of skin cancers, such as melanoma and focal cell carcinoma. The recognition and the classification of skin malignant growth in the beginning time is expensive and challenging. The deep learning architectures such as recurrent networks and convolutional neural networks (ConvNets) are developed in the past, which are proven appropriate for non-handcrafted extraction of complex features. To additional expand the efficiency of the ConvNet models, a cascaded ensembled network that uses an integration of ConvNet and handcrafted features based multi-layer perceptron is proposed in this work. This offered model utilizes the convolutional neural network model to mine nonhandcrafted image features and colour moments and texture features as handcrafted features. It is demonstrated that accuracy of ensembled deep learning model is improved to 98.3% from 85.3% of convolutional neural network model.

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Development of Mobile Skin Cancer Detection using Faster R-CNN and MobileNet v2 Model

Cited by 33 publications

References 15 publications

Convolution Neural Network Method for Skin Cancer Diagnosis: Comparison and Improvement

Convolution Neural Network Method for Skin Cancer Diagnosis: Comparison and Improvement

MSDSC: A Multistage Deep Learning-based Skin Cancer Classifier

Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network

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