The process of turning forest area into land is known as deforestation or forest degradation. Reforestation as a fraction of deforestation is extremely low. For improved qualitative and quantitative classification, we...The process of turning forest area into land is known as deforestation or forest degradation. Reforestation as a fraction of deforestation is extremely low. For improved qualitative and quantitative classification, we used Sentinel-1 dataset of State of Para, Brazil to precisely and closely monitor deforestation between June 2019 and June 2023. This research aimed to find out suitable model for classification called Satellite Imaging analysis by Transpose deep neural transformation network (SIT-net) using mathematical model based on Band math approach to classify deforestation applying transpose deep neural network. The main advantage of proposed model is easy to handle SAR images. The study concludes that SAR satellite gives high-resolution images to improve deforestation monitoring and proposed model takes less computational time compared to other techniques.展开更多
Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-...Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images.Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images.Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection.Nowadays,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer.This study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology images.Resnet50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test accuracies.The result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,respectively.Classification accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data results.Average precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.展开更多
文摘The process of turning forest area into land is known as deforestation or forest degradation. Reforestation as a fraction of deforestation is extremely low. For improved qualitative and quantitative classification, we used Sentinel-1 dataset of State of Para, Brazil to precisely and closely monitor deforestation between June 2019 and June 2023. This research aimed to find out suitable model for classification called Satellite Imaging analysis by Transpose deep neural transformation network (SIT-net) using mathematical model based on Band math approach to classify deforestation applying transpose deep neural network. The main advantage of proposed model is easy to handle SAR images. The study concludes that SAR satellite gives high-resolution images to improve deforestation monitoring and proposed model takes less computational time compared to other techniques.
文摘Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images.Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images.Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection.Nowadays,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer.This study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology images.Resnet50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test accuracies.The result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,respectively.Classification accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data results.Average precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.
