Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top caus...Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death worldwide.The malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival rates.The study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological images.It uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the lung.In addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and epochs.The proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images.This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.展开更多
文摘Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death worldwide.The malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival rates.The study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological images.It uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the lung.In addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and epochs.The proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images.This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.
