| الوصف: |
Despite being the second most often diagnosed form of cancer, lung cancers are rarely found in the general population. It is proposed in this study to use CT scan images to extract a global as well as local feature extraction methodology for the identification of lung cancer. Data gathering, globalised and localised training as well as testing the model are all part of this structure. This study makes use of 800 CT scan images. An image is pre-processed by warping and cropping in advance of the global testing step. Each image is represented by a feature vector employing eight distinct types of image characteristics, which are taken from the images. After creating feature vectors, three machine learning methods are employed to create detection models. Every medical image has been partitioned over series of simple divisions throughout the training and testing process locally. To describe each block, feature vectors are derived from the image features that worked effectively in the general phase of the experiment. Similar extracted features are then used to build detection systems for all of the picture blocks using the learning strategies that were effective in the global stage. SVM using Haar Wavelet characteristics had an accuracy, sensitivity, and specificity of 89%, 90%, and 89%, respectively. One might get 90% accurate results with SVM and 91% sensitive and 91% specific results using SVM plus HOG features. Finally, utilising SVM having the Gabor Filter characteristics got the greatest correctness, specificity, and sensitivity values as 87 percent, 86 percent, and 87 percent. |