A Deep learning based patient care application for skin cancer detection

Dong, Hanlin; Farid, Farnaz

doi:10.21203/rs.3.rs-1582255/v1

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…System CNN and RNN architectures were tested on the dermatology dataset. Based on conducted experiments, user best class call using CNN, an AUROC of 81.6% per patient; this result is roughly 5% higher than seeing a similar comparison across the same number of data [22].…”

Section: Literature Reviewmentioning

confidence: 85%

Skin Type Detection with Deep Learning: A Comparative Analysis

KARA

Kara

ÇELİK³

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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There are many factors that can change and affect appearance, including age and environment. Knowing the skin type helps to choose the products best suited to the needs of the skin and therefore the right skin care. Recently, the increasing demand for cosmetics and the scarcity of well-equipped cosmetologists have encouraged cosmetology centers to meet the need by using artificial intelligence applications. Deep learning applications can give high accuracy results in determining the skin type. Recent research shows that learning performs better on nonlinear data than machine learning methods. The aim of this study is to find the best classification model for skin type prediction in skin analysis data with deep learning. For this purpose, 4 different optimization algorithms as Sgd, Adagrad, Adam and Adamax; Tanh and ReLU activation functions and combinations of different neuron numbers using, 16 different models were created.In experimental studies, the performance of the models varies according to the parameters, and it has been observed that the most successful deep neural network model is the model consisting of 64 neurons, Sgd optimization function and ReLU activation function combination with a success rate of 93.75. The accuracy result obtained has a higher classification success compared to other methods, and shows that deep neural networks can make an accurate skin type classification.

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Section: Literature Reviewmentioning

confidence: 85%

Skin Type Detection with Deep Learning: A Comparative Analysis

KARA

Kara

ÇELİK³

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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“…The lifetime risk of being affected by melanoma is about 2.6% (1 in 38) for whites, 0.1% (1 in 1,000) for Blacks, and 0.6% (1 in 167) for Hispanics [5]. The early-stage detection of melanoma skin cancer can be beneficial in curing it [3,11,12,37]. The early diagnosis survival rate of skin cancer is more than 90 percent [2].…”

Section: Introductionmentioning

confidence: 99%

Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network

Ashafuddula,

Islam

2023

csci

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Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), Sensitivity (93.76%), Specificity (91.56%), and Precision (90.68%) are achieved.

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Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection

Nasir,

Tehsin,

Damaševičius

et al. 2024

Algorithms

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Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, showing encouraging outcomes in terms of enhancing diagnostic precision. In this paper, multimodal Explainable Artificial Intelligence (XAI) is presented that offers explanations that (1) address a gap regarding interpretation by identifying specific dermoscopic features, thereby enabling (2) dermatologists to comprehend them during melanoma diagnosis and allowing for an (3) evaluation of the interaction between clinicians and XAI. The specific goal of this article is to create an XAI system that closely aligns with the perspective of dermatologists when it comes to diagnosing melanoma. By building upon previous research on explainability in dermatology, this work introduces a novel soft attention mechanism, called Convolutional Spiking Attention Module (CSAM), to deep neural architectures, which focuses on enhancing critical elements and reducing noise-inducing features. Two instances of the proposed CSAM were placed inside the proposed Spiking Attention Block (SAB). The InceptionResNetV2, DenseNet201, and Xception architectures with and without the proposed SAB mechanism were compared for skin lesion classification. Pretrained networks with SAB outperform state-of-the-art methods on the HAM10000 dataset. The proposed method used the ISIC-2019 dataset for the crossdataset validation process. The proposed model provides attention regarding cancer pixels without using an external explainer, which proves the importance and significance of the SAB module.

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A Deep learning based patient care application for skin cancer detection

Cited by 3 publications

References 26 publications

Skin Type Detection with Deep Learning: A Comparative Analysis

Skin Type Detection with Deep Learning: A Comparative Analysis

Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network

Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection

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