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LuNet-LightGBM:An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Computer Systems Science & Engineering 2023年第7期46卷 597-617页

作者： Sesikala Bapatla j.harikiran School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra Pradesh522237India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237India

Diabetes problems can lead to an eye disease called Diabetic Retinopathy(DR),which permanently damages the blood vessels in the *** not treated early,DR becomes a significant reason for *** identify the DR and determine the stages,medical tests are very labor-intensive,expensive,and *** address the issue,a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this *** proposal is based on lesion segmentation of the fundus images based on the LuNet *** a Refined Attention Pyramid Network(RAPNet)is used for extracting global and local *** increase the performance of the classifier,the unique features are selected from the extracted feature set using Aquila Optimizer(AO)***,the LightGBM model is applied to classify the input image based on the *** investigations have been done to analyze the performance of the proposed framework on three publically available datasets(MESSIDOR,APTOS,and IDRiD)using several performance metrics such as accuracy,precision,recall,and *** proposed classifier achieves 99.29%,99.35%,and 99.31%accuracy for these three datasets *** outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

关键词： Deep learning diabetic retinopathy DR grading lightGBM LuNet lesion segmentation

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Software Defect Prediction Based Ensemble Approach

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Computer Systems Science & Engineering 2023年第6期45卷 2313-2331页

作者： j.harikiran B.Sai Chandana B.Srinivasarao B.Raviteja Tatireddy Subba Reddy School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237India Department of Computer Science and Engineering GITAM Deemed to be UniversityTelangana502329India Department of Computer Science and Engineering B.V.Raju Institute of Technology NarsapurMedakTelangana502313India

Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance *** it comes to lowering software costs and assuring software quality,SDP plays a critical role in software *** a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more *** methods for detecting and addressing such flaws at a low cost have been *** approaches,on the other hand,need to be significantly improved in terms of *** in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are *** proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative *** find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository *** compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the jM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.

关键词： Prediction of a software defect deep learning models enhancedWOA firefly algorithm

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