Background:Skin cancer is a major cause of mortality,and early detection is vital for effective treatment.Diagnosis is challenging because of lesion variability.This study adapts VINCE-NET,a hybrid deep-learning model...Background:Skin cancer is a major cause of mortality,and early detection is vital for effective treatment.Diagnosis is challenging because of lesion variability.This study adapts VINCE-NET,a hybrid deep-learning model originally designed for stroke detection,to classify melanoma using dermoscopic images.Methods:VINCE-NET combines vision transformer layers for global context,convolutional neural networks for local features,and long short-term memory for spatial sequence modeling.During preprocessing,Gaussian blur,normalization,and augmentation were applied to reduce noise and handle class imbalance.During training,the public HAM10000 dataset was used in a central processing unit-only Google Colab environment(12.72 GB random access memory,107.7 GB disk)with an AdamW optimizer,a batch size of 12,learning-rate scheduling,and early stopping(patience=50).VINCE-NET's performance was compared with those of a convolutional neural networks,long short-term memory,residual network with 50 layers(ResNet-50),visual geometry group network with 16 and 19 layers(VGG-16/19),and densely connected convolutional network with 121 and 201 layers(DenseNet-121/201)under identical preprocessing conditions.Results:VINCE-NET achieved 94.1%accuracy,95.5% precision,90.4% recall,a 92.9% F1-score,and an area under the receiver operating characteristic curve of 0.98 at a training time of 34,308.42 s.Benchmarks showed that VINCE-NET outperformed baselines while being computationally efficient.Conclusion:VINCE-NET provides competitive performance for melanoma classification and feasibility in resource-limited settings.Although promising,VINCE-NET has not been clinically validated yet.Future work will address resolution,ablation studies,interpretability,and external validation.展开更多
The objective of this study is to process the soil images to generate a digital soil classification system for rural farmers at low cost.Soil texture is the main factor to be considered before doing cultivation.It aff...The objective of this study is to process the soil images to generate a digital soil classification system for rural farmers at low cost.Soil texture is the main factor to be considered before doing cultivation.It affects the crop selection and regulates the water transmission property.The conventional hydrometer method determines the percentage of sand,silt,and clay present in a soil sample.This method is very cost and time-consuming process.In this approach,we collect 50 soil samples from the different region of west Guwahati,Assam,India.The samples are photographed under a constant light condition using an Android mobile of 13 MP cameras.The fraction of sand,silt,and clay of the soil samples are determined using the hydrometer test.The result of the hydrometer test is processed with the United State Department of Agriculture soil classification triangle for the final soil classification.Soil images are processed through the different stages like pre-processing of soil images for image enhancement,extracting the region of interest for segmentation and the texture analysis for feature vector.The feature vector is calculated from the Hue,Saturation,and Value(HSV)histogram,color moments,color auto Correlogram,Gabor wavelets,and discrete wavelet transform.Finally,Support Vector Machine classifier is used to classify the soil images using linear kernel.The proposed method gives an average of 91.37%accuracy for all the soil samples and the result is nearly the same with the United State Department of Agriculture soil classification.展开更多
文摘Background:Skin cancer is a major cause of mortality,and early detection is vital for effective treatment.Diagnosis is challenging because of lesion variability.This study adapts VINCE-NET,a hybrid deep-learning model originally designed for stroke detection,to classify melanoma using dermoscopic images.Methods:VINCE-NET combines vision transformer layers for global context,convolutional neural networks for local features,and long short-term memory for spatial sequence modeling.During preprocessing,Gaussian blur,normalization,and augmentation were applied to reduce noise and handle class imbalance.During training,the public HAM10000 dataset was used in a central processing unit-only Google Colab environment(12.72 GB random access memory,107.7 GB disk)with an AdamW optimizer,a batch size of 12,learning-rate scheduling,and early stopping(patience=50).VINCE-NET's performance was compared with those of a convolutional neural networks,long short-term memory,residual network with 50 layers(ResNet-50),visual geometry group network with 16 and 19 layers(VGG-16/19),and densely connected convolutional network with 121 and 201 layers(DenseNet-121/201)under identical preprocessing conditions.Results:VINCE-NET achieved 94.1%accuracy,95.5% precision,90.4% recall,a 92.9% F1-score,and an area under the receiver operating characteristic curve of 0.98 at a training time of 34,308.42 s.Benchmarks showed that VINCE-NET outperformed baselines while being computationally efficient.Conclusion:VINCE-NET provides competitive performance for melanoma classification and feasibility in resource-limited settings.Although promising,VINCE-NET has not been clinically validated yet.Future work will address resolution,ablation studies,interpretability,and external validation.
文摘The objective of this study is to process the soil images to generate a digital soil classification system for rural farmers at low cost.Soil texture is the main factor to be considered before doing cultivation.It affects the crop selection and regulates the water transmission property.The conventional hydrometer method determines the percentage of sand,silt,and clay present in a soil sample.This method is very cost and time-consuming process.In this approach,we collect 50 soil samples from the different region of west Guwahati,Assam,India.The samples are photographed under a constant light condition using an Android mobile of 13 MP cameras.The fraction of sand,silt,and clay of the soil samples are determined using the hydrometer test.The result of the hydrometer test is processed with the United State Department of Agriculture soil classification triangle for the final soil classification.Soil images are processed through the different stages like pre-processing of soil images for image enhancement,extracting the region of interest for segmentation and the texture analysis for feature vector.The feature vector is calculated from the Hue,Saturation,and Value(HSV)histogram,color moments,color auto Correlogram,Gabor wavelets,and discrete wavelet transform.Finally,Support Vector Machine classifier is used to classify the soil images using linear kernel.The proposed method gives an average of 91.37%accuracy for all the soil samples and the result is nearly the same with the United State Department of Agriculture soil classification.
