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Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classicatio

Computers, Materials & Continua 2021年第7期68卷 1099-1116页

作者： Ayesha Bin T.Tahir muhamamd attique khan Majed Alhaisoni Junaid Ali khan Yunyoung Nam Shui-Hua Wang Kashif Javed Department of Computer Science HITEC UniversityTaxila47040Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUUK Department of Robotics SMME NUSTIslamabadPakista

Background:A brain tumor reects abnormal cell ***:Surgery,radiation therapy,and chemotherapy are used to treat brain tumors,but these procedures are painful and *** resonance imaging(MRI)is a non-invasive modality for diagnosing tumors,but scans must be interpretated by an expert ***:We used deep learning and improved particle swarm optimization(IPSO)to automate brain tumor *** scan contrast is enhanced by ant colony optimization(ACO);the scans are then used to further train a pretrained deep learning model,via transfer learning(TL),and to extract features from two dense *** fused the features of both layers into a single,more informative *** IPSO algorithm selected the optimal features,which were classied using a support vector ***:We analyzed high-and low-grade glioma images from the BRATS 2018 dataset;the identication accuracies were 99.9%and 99.3%,***:The accuracy of our method is signicantly higher than existing techniques;thus,it will help radiologists to make diagnoses,by providing a“second opinion.”

关键词： Brain tumor contrast enhancement deep learning feature selection classication

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Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

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Computers, Materials & Continua 2021年第12期69卷 2917-2932页

作者： Muhammad Rizwan Latif muhamamd attique khan Muhammad Younus Javed Haris Masood Usman Tariq Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan Wah Engineering College University of WahWah Cantt47080Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton *** 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton *** 1.5 million people in Pakistan are engaged in the cotton value ***,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton *** diagnosis is not a good solution due to several factors such as high cost and unavailability of an ***,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early *** this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature *** proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further *** final recognition,a Cubic SVM approach was utilized and validated on a prepared *** the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework..

关键词： Plants diseases data augmentation deep learning features fusion features selection

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A Two Stream Fusion Assisted Deep Learning Framework for Stomach Diseases Classification

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Computers, Materials & Continua 2022年第11期73卷 4423-4439页

作者： Muhammad Shahid Amin Jamal Hussain Shah Mussarat Yasmin Ghulam Jillani Ansari muhamamd attique khan Usman Tariq Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Department of Information Sciences University of Education Lahore(Multan Campus)Pakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary *** technique is sensitive to these ***,fake samples cause AI and DL model to produce diverse *** attacks that successfully implemented in real world scenarios highlight their applicability even *** this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks ***,such attacks and defensive strategies are gaining lot of attention by the machine learning and security *** use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding *** a result,it took weeks to generate patients test report,which is tiring and strenuous for ***,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such *** far as the classification is concern,the adversarial attacks generate problems in classified *** days,to handle this issue machine learning is mainstream defensive approach against adversarial ***,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid *** obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.

关键词： WCE images adversarial attacks FGSM noise salt and pepper noise feature fusion deep learning

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Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

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Computers, Materials & Continua 2022年第5期71卷 2477-2495页

作者： Amina Bibi muhamamd attique khan Muhammad Younus Javed Usman Tariq Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa Rasha H.Sakr Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Computer Science Department Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt

Background:In medical image analysis,the diagnosis of skin lesions remains a challenging *** lesion is a common type of skin cancer that exists *** is one of the latest technologies used for the diagnosis of skin ***:Many computerized methods have been introduced in the literature to classify skin ***,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant *** Work:In this study,a new technique is proposed based on the conventional and deep learning *** proposed framework consists of two major tasks:lesion segmentation and *** the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color ***,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding *** the lesion classification task,two pre-trained CNN models were modified and trained using transfer *** features were extracted from both models and fused using canonical correlation *** the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this *** features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final ***:The experimental process was conducted on two datasets:ISIC 2017 and *** ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification *** achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques.

关键词： Skin cancer lesion segmentation deep learning features fusion classification

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