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A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

Intelligent Automation & Soft Computing 2024年第1期39卷 57-71页

作者： Ghulam Gilanie Sana Cheema akkasha latif AnumSaher Muhammad Ahsan Hafeez Ullah Diya Oommen Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurBahawalpur63100Pakistan West High School Salt Lake CityUT84103USA

Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the *** affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of ***,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of *** this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based *** Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry *** have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental *** experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,***,the outcomes demonstrated the stated method’s superiority over the current best practices.

关键词： Bipolar mental illness detection facial micro-expressions facial landmarked images

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A Deep Learning-Based Automated Approach of Schizophrenia Detection from Facial Micro-Expressions

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Intelligent Automation & Soft Computing 2024年第6期39卷 1053-1071页

作者： Anum Saher Ghulam Gilanie Sana Cheema akkasha latif Syeda Naila Batool Hafeez Ullah Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurBahawalpur63100Pakistan

Schizophrenia is a severe mental illness responsible for many of the world’s *** significantly impacts human society;thus,rapid,and efficient identification is *** research aims to diagnose schizophrenia directly from a high-resolution camera,which can capture the subtle micro facial expressions that are difficult to spot with the help of the naked *** a clinical study by a team of experts at Bahawal Victoria Hospital(BVH),Bahawalpur,Pakistan,there were 300 people with schizophrenia and 299 healthy *** of these participants have been captured and converted into their frames using the OpenFace ***,pose,gaze,Action Units(AUs),and land-marked features have been extracted in the Comma Separated Values(CSV)*** faces have been used to detect schizophrenia by the proposed and the pre-trained Convolutional Neural Network(CNN)models,i.e.,VGG16,Mobile Net,Efficient Net,Google Net,and ***,Vision transformer,Swim transformer,big transformer,and vision transformer without attention have also been used to train the models on customized *** files have been used to train a model using logistic regression,decision trees,random forest,gradient boosting,and support vector machine ***,the parameters of the proposed CNN architecture have been optimized using the Particle Swarm Optimization *** experimental results showed a validation accuracy of 99.6%for the proposed CNN *** results demonstrated that the reported method is superior to the previous *** model can be deployed in a real-time environment.

关键词： Schizophrenia deep learning machine learning facial expressions transformers particle swarm optimization(PSO)algorithm

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