چکیده مقاله
Colorectal cancer is recognized as oneof the most common cancers of the present era Early detection of this type of cancer cansignificantly facilitate doctors' decision makingand reduce their workload Recently, the successof artificial neural networks in identifying andclassifying disease lesions has encouragedresearchers to use this method for processingmedical images The present study wasconducted with the primary aim of presentingan artificial neural network algorithm to detectcolorectal cancer in medical images Morespecifically, colorectal cancer is a commonmalignancy, and accurate tissue analysis is vitalfor diagnosis and treatment planning In thisstudy, we use transfer learning and the VGG16convolutional neural network to classify tissuesin histopathological images of colorectal cancer Using the Kather_texture_2016 dataset, whichcontains 5,000 histology images classified intoeight types of tissues, we preprocess andaugment the data to increase the model'sgeneralization Our approach integrates a pre trained VGG16 model that is fine tuned with additional custom layers to extract robustfeatures and achieve high classificationaccuracy The model is trained and validatedusing a precise split of training, validation, andtest sets Our results show significantperformance with 92% accuracy and a Cohen'skappa score of 0 91, indicating strong agreementwith the actual labels This study emphasizes thepotential of deep learning and transfer learningin advancing the accuracy of colorectalhistopathological analysis, contributing to morereliable and efficient diagnostic processes
کلیدواژهها
نویسندگان
Solmaz Ebadi
surgical technologist Operating room field Tehran Azad University of Medical SciencesTabriz, IRAN
Saeid Alizadeh
Electronic Engineering DepartmentRumeli UniversityIstanbul, Turkiye
شیوه ارجاع
Ebadi, Solmaz and Alizadeh, Saeid,1403,Modeling and Detection of Colorectal Cancer ImagesUsing Transfer Learning and Convolutional NeuralNetworks (VGG16),19th International Conference on Innovation and Research in Engineering Sciences (ICIRES)
ارائهشده در
مجموعه مقالات نوزدهمین کنفرانس بین المللی نوآوری و تحقیق در علوم مهندسی15 آذر 1403