مشروع البحث: EVRİŞİMSEL SİNİR AĞLARI VE ÇEKİRGE OPTİMİZASYON ALGORİTMASI KULLANARAK KOLON KANSER HASTALIĞI TESBİTİ
تحميل...
المساهمين
الممولين
رقم التعريف
246
الباحث
AMNA ALI A MOHAMED
الوصف
optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and di-agnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features as-sociated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction tech-niques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1 score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.
الكلمات الدالة
KEYWORDS:Colon Cancer Disease Diagnosis, Convolutional Neural Network, Grasshopper Optimization Algorithm, Machine Learning