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Early Detection of Diabetic Retinopathy Using Machine Intelligence throughDeep Transfer and Representational Learning

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Computers, Materials & Continua 2021年第2期66卷 1631-1645页

作者： fouzia nawaz Muhammad Ramzan Khalid Mehmood Hikmat Ullah Khan Saleem Hayat Khan Muhammad Raheel Bhutta Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan and School of Systems and TechnologyUniversity of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabadPakistan University Hospital Birmingham BirminghamUK Shifa International Hospital IslamabadPakistan Department of Computer Science and Engineering Sejong UniversitySeoulKorea

Diabetic retinopathy (DR) is a retinal disease that causes irreversible *** occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appear at the initial level. To preventblindness, early detection and regular treatment are needed. Automated detectionbased on machine intelligence may assist the ophthalmologist in examining thepatients’ condition more accurately and efficiently. The purpose of this study is toproduce an automated screening system for recognition and grading of diabetic retinopathyusing machine learning through deep transfer and representational *** artificial intelligence technique used is transfer learning on the deep neural network,Inception-v4. Two configuration variants of transfer learning are applied onInception-v4: Fine-tune mode and fixed feature extractor mode. Both configurationmodes have achieved decent accuracy values, but the fine-tuning method outperformsthe fixed feature extractor configuration mode. Fine-tune configuration modehas gained 96.6% accuracy in early detection of DR and 97.7% accuracy in gradingthe disease and has outperformed the state of the art methods in the relevant literature.

关键词： Diabetic retinopathy artificial intelligence automated screening system machine learning deep neural network transfer and representational learning

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A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 1721-1736页

作者： Ali Alqahtani Shumaila Akram Muhammad Ramzan fouzia nawaz Hikmat Ullah Khan Essa Alhashlan Samar MAlqhtani Areeba Waris Zain Ali College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Department of Medical Imaging King Khaled HospitalNajran61441Saudi Arabia Electrical&Electronics Engineering Department Universiti TeknologiPetronas31750Malaysia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its *** is a signiﬁ-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most *** overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of ***-ever,this method burdens the radiologist to examine each ***,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of *** proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 *** proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays *** dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical *** results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

关键词： COVID-19 transfer learning deep learning artiﬁcial intelligence chest X-rays machine learning

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Synthesis, Crystal Structure and Antimicrobial Activity of {[Bis(pyridine-κN)bis(3,5-dinitrosalicylato κ-O,O')Zn(Ⅱ)][bis(pyridine-κN)Zn(Ⅱ)]}

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Chinese Journal of Structural Chemistry 2016年第5期35卷 705-712页

作者： CHANG fouzia MEMON Najma AHMED Muhammad Naeem TAHIR Muhammad nawaz MEMON Shahabuddin CHANG Abdul Sattar BHANGER Muhammad Iqbal CHANDIO Anwer National Centre of Excellence in Analytical Chemistry University of Sindh Department of Chemistry The University of Azad Jammu and Kashmir Department of Physics University of Sargodha HEJ Research Institute of Chemistry ICCBS University of Karachi

A novel {[bis（pyridine-κN）bis（3,5-dinitrosalicylato κ-O,O＇）Zn（Ⅱ）][bis（pyridine-κN）Zn（Ⅱ）]}（C_（34）H_（24）N_8O_（14）Zn_2） was synthesized by a self-assemble method at room temperature. The molecular structure was determined by single-crystal X-ray analysis. The compound crystallizes in the monoclinic system, space group P2_1/n with a = 12.2156, b = 13.5696, c = 22.5602 A, β = 90.061o, Z = 4 and V = 3739.6（3）A^3. The new 1D binuclear coordination polymer {[bis（py-κN）bis（3,5-dinitrosal κ-O,O′） Zn（Ⅱ）][bis（py-κN）Zn（Ⅱ）]} resulted from two different types of moieties. The polymer [bis（py-κN）bis（3,5-dinitrosal κ-O,O′）Zn（Ⅱ）] unit is connected with [bis（py-κN） Zn（Ⅱ）] by zigzag topology. One zinc（Ⅱ） cation has a six-fold coordination environment, in which the metal atom is connected with four oxygen atoms of two 3,5-dinitrosalicylic acids to form equatorial bonds and two nitrogen atoms of pyridine to generate the axial bonds. Other four-fold nucleus contain two Zn-O bonds from different 3,5-dinitrosalicylic acids and two bonds with the Natom of pyridine. Antimicrobial assay results indicated that the compound showed moderate activities against different bacterial and fungal strains.

关键词： crystal structure antimicrobial pyridine C-H…π interactions

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