Current cancer diagnosis procedure requires expert knowledge and is time-consuming,which raises the need to build an accurate diagnosis support system for lymphoma identification and classification.Many studies have s...Current cancer diagnosis procedure requires expert knowledge and is time-consuming,which raises the need to build an accurate diagnosis support system for lymphoma identification and classification.Many studies have shown promising results using Machine Learning and,recently,Deep Learning to detect malignancy in cancer cells.However,the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem.In literature,many attempts were made to classify up to four simple types of lymphoma.This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma.These Lymphoma types are Classical Hodgkin Lymphoma,Nodular Lymphoma Predominant,Burkitt Lymphoma,Follicular Lymphoma,Mantle Lymphoma,Large B-Cell Lymphoma,and T-Cell Lymphoma.Our proposed approach uses Residual Neural Networks,ResNet50,with a Transfer Learning for lymphoma’s detection and classification.The model used results are validated according to the performance evaluation metrics:Accuracy,precision,recall,F-score,and kappa score for the seven multi-classes.Our algorithms are tested,and the results are validated on 323 images of 224×224 pixels resolution.The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.展开更多
In this study a visual machine technology-based intelligent system was developed and evaluated for separation and recognizing the alive and dead eggs of rainbow trout fish.The features derived from imagery processing ...In this study a visual machine technology-based intelligent system was developed and evaluated for separation and recognizing the alive and dead eggs of rainbow trout fish.The features derived from imagery processing of alive and dead eggs were used as the decision-making variables in the classifier.Multi-layer Perceptron neural network(MLP)and Support Vector Machine(SVM)models were used as the classifiers.With paired t-test,10 effective features were selected from 15 features for classification.The k-fold cross validation method was used for better evaluation the classifiers.By changing the size of the training data set from 80%to 20%,the classifier ability and stabilitywere evaluated.The results showed that in the training phase,all the mean values of the statistical indices forMLP and SVMclassificationswere complete for all categories(100%of the classification was predicted correctly).Also,in the test phase,the performance indicators of both classifiers were very satisfactory(the average accuracywas 99.45%).Therefore,it is possible to use both classifierswith certainty for separation the rainbow trout fish eggs.展开更多
文摘Current cancer diagnosis procedure requires expert knowledge and is time-consuming,which raises the need to build an accurate diagnosis support system for lymphoma identification and classification.Many studies have shown promising results using Machine Learning and,recently,Deep Learning to detect malignancy in cancer cells.However,the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem.In literature,many attempts were made to classify up to four simple types of lymphoma.This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma.These Lymphoma types are Classical Hodgkin Lymphoma,Nodular Lymphoma Predominant,Burkitt Lymphoma,Follicular Lymphoma,Mantle Lymphoma,Large B-Cell Lymphoma,and T-Cell Lymphoma.Our proposed approach uses Residual Neural Networks,ResNet50,with a Transfer Learning for lymphoma’s detection and classification.The model used results are validated according to the performance evaluation metrics:Accuracy,precision,recall,F-score,and kappa score for the seven multi-classes.Our algorithms are tested,and the results are validated on 323 images of 224×224 pixels resolution.The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.
基金Financial supports fromthe Ferdowsi University of Mashhad and Agricultural Sciences and Natural Resources University of Khuzestan,are highly appreciated.
文摘In this study a visual machine technology-based intelligent system was developed and evaluated for separation and recognizing the alive and dead eggs of rainbow trout fish.The features derived from imagery processing of alive and dead eggs were used as the decision-making variables in the classifier.Multi-layer Perceptron neural network(MLP)and Support Vector Machine(SVM)models were used as the classifiers.With paired t-test,10 effective features were selected from 15 features for classification.The k-fold cross validation method was used for better evaluation the classifiers.By changing the size of the training data set from 80%to 20%,the classifier ability and stabilitywere evaluated.The results showed that in the training phase,all the mean values of the statistical indices forMLP and SVMclassificationswere complete for all categories(100%of the classification was predicted correctly).Also,in the test phase,the performance indicators of both classifiers were very satisfactory(the average accuracywas 99.45%).Therefore,it is possible to use both classifierswith certainty for separation the rainbow trout fish eggs.
