## 3 Related Work  
Oncology computer aided diagnostic tools have evidently evolved over the years. ML-based tissue and tumor segmentation models have been widely proposed as auxiliary diagnostic tools. Two examples of the recent work on segmentation are [PERSON] et al. [13] and [PERSON] et al. [14]. In [PERSON] et al. [13] approach, adaptive global threshold is applied for lung segmentation. The threshold is elected based on CT image histogram. Following segmentation, laplacian pyramids are used for image decomposition and reconstruction. Adaptive Sparse representation is used for image fusion. [PERSON] et al. [14] used transfer learning pretrained on an artificial dataset LUNA16. The artificial dataset was generated with the aid of Generative Adversarial Network and 3D graph cut. The pretrained segmentation model is constructed by nnUnet, where it showed 0.09 higher dice similarity score than without transfer learning. Despite that lung segmentation is an important step for subsequent diagnosis, tools that perform solely segmentation fail to utilize the full capability of ML. Hence, various studies were directed towards providing a complete solution for pathology diagnosis and/or stage assessment. To this end, [PERSON] et al. [15] compared the performance of fully connected VGG16 Convolution Neural Network (CNN) classification to traditional ML classifiers. The classification models were used to differentiate between ADC and SCC. The traditional classifiers relied on 512 and 4096 feature vectors output from VGG16 architecture. The highest performance was achieved through a deep-radiomics approach, where a 4096 feature vector is obtained from the last fully connected layer. Principal component analysis and Least Absolute Shrinkage and Selection Operator (LASSO) method were used to generate and select the most relevant features. Then, k-Nearest Neighbors (kNN) (k = 5) is applied for classification. [PERSON] et al. [16] conducted an extensive comparison between various approaches: two classifiers radiomics (kNN and SVM), four recent CNN architectures, CNN with Long Short Term Memory (LSTM) and combinatorial approach (CNN + LSTM + radiomics). LSTM was used to incorporate spatial coherence of CT slices into the model. LSTM + Inception attained  
Figure 1: NSCLC radiopathological subtypes manifestations (marked) on CT scans: (**a**) Adenocarcinoma (**b**) Squamous Cell Carcinoma (**c**) Large Cell (undifferentiated) Carcinoma. the highest accuracy and AuC. Another study based on radiomics is the work of [PERSON] et al. [17]. A set of 1433 radiomics features were generated from wavelet decomposition and LOG filtered images. Wrapper and Multivariate regression feature selection algorithms were applied to classify histopathological subtypes. Traditional learning was also adopted by [PERSON] et al. [18]. A total of 788 radiomic features were extracted then various features selection methods were used to select the most informative features. Logistics Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) were applied for histology (SCC and ADC) determination. Other studies were directed towards staging, such as the work of [PERSON] et al. [19]. Image segmentation and features extraction are done using 3D-Slicer software and Pyradiomics package respectively. SMOTE oversampling was applied to balance the dataset. Random Forest was used for staging. The model did not perform well on multi-class staging, hence binary (early-late) staging was performed resulting in accuracy of 75%. [PERSON] et al. [20] also performed binary staging with a serial two phase system. U-net autoencoder network is used for latent variables extraction and image reconstruction. Then, CNN architecture is used for binary classification. U-net + CNN outperformed U-net + traditional machine learning classifiers. [PERSON] et al. [21] performed T-staging using Back Propagation Neural Network (BPNN). Five classifiers were applied on merged benchmark datasets.