DNA Microarray for Cancer Classification Using Deep Learning Based on RESNET-50 Convolutional Neural Network Technique
Keywords:
DNA, Microarray, cancer classification, Image processing, ResNet-50 CNN, and Enhanced Canny Contour DetectionAbstract
In today's world, microarray data structures are significant in the diagnosis and classification of various malignant tissues and diseases. Therefore, to effectively address the difficulties of gene expression and classification, it offers high dimensionality and a limited number of genetic samples. Gene expression datasets are frequently utilized in disease prediction and diagnosis, particularly in cancer treatment. Medical diagnostics is one of the most important fields in which image-processing techniques are utilized effectively. Moreover, image processing is vital for enhancing diagnostic and surgical precision. However, accurately characterizing cancer biologically and identifying disease-causing gene expression can be challenging and time-consuming. To address this issue, we introduce the ResNet-50 Convolutional Neural Network (ResNet-50 CNN) method to increase the accuracy of DNA microarray cancer classification. Furthermore, we pre-process the image using the Adaptive Median Filter (AMF) method to remove background noise and enhance a smooth image. Additionally, the pixel-based color contrast, texture, and local contrast of blood cell cancer images are all enhanced using the Contrast-Limited Adaptive Histogram Equalization (CLAHE) technique. After that, we employ the Enhanced Canny Edge Detection (ECDD) algorithm to improve edge detection and reduce the error rate in image segmentation. Finally, we propose a ResNet-50 CNN method using a Deep Learning (DL) algorithm to improve the accuracy of classifying DNA microarrays as either cancerous or non-cancerous. Furthermore, the introduced algorithm can be used to classify microarray cancers through performance evaluations such as precision, recall, precision, time complexity, and F1 score. The results of the proposed model indicate an efficiency rating of 96.8% when assessed using various performance measures.
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