Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.

Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%sensitivity.On top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%accuracy.The proposed system is a built-in cascading manner with five models of polynomial(SVM)classifier.The overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

Pulmonary Diseases Decision Support System Using Deep Learning Approach

Pulmonary diseases are common throughout the world,especially in developing countries.These diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently COVID-19.In general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists.In recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases.In this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was analyzed.To the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)images.All models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross validation.Using individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors.