Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep lea...Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.展开更多
The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Va...The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Various research has been done for pornographic image detection and classification.Most of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine learning.Therefore,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic vs.Nonpornographic)classification in images.The simulation result shows that VGG-16 performed better than the used model in this study without augmented data.The VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,0.16.The precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,0.11.The f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&0.46.The f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of 0.47.The f-measure,recall,and precision values for explicit and non-explicit images are(0.86,0.97,0.91)and(0.86,0.93,0.89).Where else without augmented data the VGG-16 model reached a training and validation accuracy of 0.997,0.986 with a loss of 0.008,0.056.The f-measure,recall,and precision values for explicit and non-explicit images are(0.94,0.99,0.97)and(0.99,0.93,0.96)which outperforms the used models with the augmented dataset in this study.展开更多
文摘Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.
基金funded by the Deanship of Scientific Research at Jouf University under Gran Number DSR–2022–RG–0101.
文摘The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Various research has been done for pornographic image detection and classification.Most of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine learning.Therefore,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic vs.Nonpornographic)classification in images.The simulation result shows that VGG-16 performed better than the used model in this study without augmented data.The VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,0.16.The precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,0.11.The f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&0.46.The f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of 0.47.The f-measure,recall,and precision values for explicit and non-explicit images are(0.86,0.97,0.91)and(0.86,0.93,0.89).Where else without augmented data the VGG-16 model reached a training and validation accuracy of 0.997,0.986 with a loss of 0.008,0.056.The f-measure,recall,and precision values for explicit and non-explicit images are(0.94,0.99,0.97)and(0.99,0.93,0.96)which outperforms the used models with the augmented dataset in this study.
