Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers

Alyami, Jaber; Rehman, Amjad; Sadad, Tariq; Alruwaythi, Maryam; Bahaj, Saeed Ali

doi:10.1002/jemt.24211

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…According to the findings of Ranpreet Kaur et al (Alyami et al, 2022), they developed an automated melanoma classifier based on a deep convolutional neural network (DCNN) that can reliably differentiate between benign and malignant melanoma. The design of a DCNN requires several other decisions as well, including the selection of numerous filters and the sizes of those filters, deep learning layers, the network depth, and hyper parameter tuning.…”

Section: Literature Surveymentioning

confidence: 99%

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

Arjun,

Kumar,

Dhanaraj

et al. 2024

Microscopy Res & Technique

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Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network‐Long Short‐Term Memory (RCNN‐LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN‐LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1‐score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes.Research Highlights Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

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Section: Literature Surveymentioning

confidence: 99%

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

Arjun,

Kumar,

Dhanaraj

et al. 2024

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

“…Along with this, they also have developed a mobile detection application. Medhat et al and Alyami et al [30,31] selected two cancer types from PAD-UFES-20 dataset (in which we used six classes in it) and applied a comparative study. Chen et al [32] used clinical data and visual data to classify skin lesions.…”

Section: Skin Disease Image Analysismentioning

confidence: 99%

“…In the literature, there are some other studies, such as Medhat et al and Alyami et al, [30,31] which used the same dataset. But, these studies selected some classes of the dataset and studied with fewer classes; some of them [32] merged visual data with clinical data.…”

Section: Experiments On Skin Lesion Image Classificationsmentioning

confidence: 99%

Deep Learning‐Based Skin Diseases Classification using Smartphones

Oztel,

Yolcu Oztel,

Sahin

2023

Advanced Intelligent Systems

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Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning‐based automated intelligent mobile application to detect skin disease. First, different small‐size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms.

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“…For instance, it can assist in identifying and delineating skin lesions, including moles, tumors [80], and other dermatological conditions in dermatology. Similarly, in the context of medical imaging, AlexNet can aid in identifying and delineating lesions or anomalies within organs [81][82][83], tissues [84], or anatomical structures [85], which is essential for treatment planning and disease monitoring.…”

Section: Medical Image Classification Applicationsmentioning

confidence: 99%

Review of AlexNet for Medical Image Classification

Tang,

Sun,

Wang

et al. 2023

EAI Endorsed Trans e-Learn

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In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.

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Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers

Cited by 8 publications

References 40 publications

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

Deep Learning‐Based Skin Diseases Classification using Smartphones

Review of AlexNet for Medical Image Classification

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