Integrated global and local feature extraction and classification from computerized tomography (CT) images for lung cancer classification.

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 employ a methodology of extracting both global and local features from CT scan images 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. Images are 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 a 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 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, the utilisation of SVM with Gabor Filter characteristics achieved the greatest correctness, specificity, and sensitivity values, particularly 87%, 86%, and 87%, respectively (Tab. 3, Fig. 7, Ref. 18). Keywords: feature extraction, support vector machine, lung cancer, classification, machine learning.