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Diabetic wounds and artificial intelligence:A mini-review

World Journal of Clinical Cases 2023年第1期11卷 84-91页

作者： samabia tehsin Sumaira Kausar Amina Jameel Computer Science Bahria UniversityKarachi 75260SindhPakistan Computer Science Bahria UniversityIslamabad 46000Pakistan Department of Computer Engineering Bahria UniversityIslamabad 46000Pakistan

Diabetic wound takes longer time to heal due to micro and macro-vascular *** longer healing time can lead to infections and other health *** ulcers are one of the most common diabetic *** are one of the leading cause of *** science is continuously striving for improving quality of human life.A recent trend of amalgamation of knowledge,efforts and technological advancement of medical science experts and artificial intelligence researchers,has made tremendous success in diagnosis,prognosis and treatment of a variety of *** wounds are no exception,as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective *** presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science,for diabetic wound *** for diabetic wound assessment using artificial intelligence is ***,this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound *** article also discusses the future directions for the betterment of the field that can lead to facilitate both,clinician and patients.

关键词： Diabetic wounds Artificial intelligence Foot ulcer Amputation Diagnosis Machine learning

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A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding

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Computer Systems Science & Engineering 2022年第10期43卷 27-43页

作者： Abdul Majid Sumaira Kausar samabia tehsin Amina Jameel Department of Computer Science Bahiria UniversityIslamabadPakistan Department of Software Engineering Bahiria UniversityKarachiPakistan

In recent years,a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and *** primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs *** it is required to combine segmentation and detection in a single *** many successful computer vision methods has been developed to aid scene understanding for a variety of real world *** understanding systems typically involves detection and segmentation of different natural and manmade things.A lot of research has been performed in recent years,mostly with a focus on things(a well-defined objects that has shape,orientations and size)with a less focus on stuff classes(amorphous regions that are unclear and lack a shape,size or other characteristics Stuff region describes many aspects of scene,like type,situation,environment of scene *** hence can be very helpful in scene *** methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time,accuracy and robustness for varying level of scene complexity.A robust scene understanding method has to effectively deal with imbalanced distribution of classes,overlapping objects,fuzzy object boundaries and poorly localized *** proposed method presents Panoptic Segmentation on Cityscapes ***-V2 is used as a backbone for feature extraction that is pre-trained on ***-V2 with state-of-art encoder-decoder architecture of DeepLabV3+with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and *** promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and

关键词： Panoptic segmentation instance segmentation semantic segmentation deep learning computer vision scene understanding autonomous applications atrous convolution

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Multi-Scale Network for Thoracic Organs Segmentation

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Computers, Materials & Continua 2022年第2期70卷 3251-3265页

作者： Muhammad Ibrahim Khalil samabia tehsin Mamoona Humayun N.Z Jhanjhi Mohammed A.AlZain Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information systems College of Computer and Information SciencesJouf UniversityKSA School of Computer Science and Engineering(SCE) Taylor’s UniversityMalaysia Center for Smart Society 5.0(CSS5) Faculty of Innovation and Technology(FIT)Taylor’s UniversityMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Medical Imaging Segmentation is an essential technique for modern medical *** is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical *** significant successes have been achieved in the segmentation of medical images,DL(deep learning)*** delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT *** now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to *** segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL *** have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of *** methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background *** results showed that our proposed framework can be segmented organs accurately.

关键词： Deep learning convolutional neural network computed tomography organs at risk computer-aided diagnostic

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Classification of Bone Marrow Cells for Medical Diagnosis of Acute Leukemia

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Journal on Artificial Intelligence 2022年第1期4卷 1-13页

作者： Khadija Khan samabia tehsin Bahria University Islamabad Pakistan

Leukemia is the cancer that starts in the blood cells due to the excess production of immature leucocytes that replace the cells with normal blood *** rely on their experience to determine the type and subtype of Leukemia from the blood *** people are misdiagnosed when it comes to its subtypes,the error rates can be up to 40%during the classification *** too depends on the expertise of the *** research represents a Convolutional Neural Network based medical image *** proposed technique can classify Leukemia and its five *** of the art deep learning and transfer learning techniques have been used,all of the network architectures are fine-tuned followed by feature *** research can assist the pathologist in decision making or can be used for the process *** proposed methodology can give better results in terms of accuracy and is more effective than the hematologist’s visual classification in terms of time.

关键词： Convolutional neural network transfer learning medical imaging acute leukemia

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