## 3 Related Work
BPNN achieved the highest accuracy of 90.6% with 28 geometric, intensity and textural nodule features. Another study that implemented cascaded deep networks is that of [PERSON] et al. [22]. Maximally stable extremal regions (MSER) and speeded up robust features (SURF) were extracted from enhanced images. The extracted features are fed to a 1D CNN-RNN model for multi-staging. The model achieved the highest accuracy compared to RF, SVM and Multilayer Perceptron. Some of the previous work relied on manual intervention and/or expert knowledge for Region of Interest (RoI) specification such as the work of [PERSON] et al. [20]. Human intervention provides validation for this step. However, fully automating the processing pipeline would mitigate the burden off clinicians and radiologists. Hence, automating the step of RoI specification is needed. The expected benefit is to enhance the performance of diagnostic systems and free clinicians and radiologists to handle more critical tasks. Also, most of the presented studies were directed towards histology classification or binary staging [15; 16; 19; 20], whereas multiclass staging can be considered more crucial. Multiclass staging is important for adapting the medical care provision plans and providing better prognosis. Also, the stratification of survival rates varies considerably with the respective stage [12]. Thus, studies are needed to address this higher complexity classification problem of multistaging to be able to provide precise treatment plans. Additionally, some of the available studies did not utilize the spatial coherence information available in 3D CT volumes [21] missing the holistic context of the slices. Neglecting the connectivity properties of CT image pixels may disadvantage the staging decision. Another limitation in 2D slice-based classification, which offers a slice by slice class, is that it does not produce a patient-level decision. Such limitation restricts these studies diagnostic value, as it does not provide an overall diagnosis per patient. Thus, 3D volumetric decision support systems are called for to produce patient decision. On the other hand, studies that used 3D volumes utilized computationally expensive architectures [15; 22]. Another issue with processing 3D volumes is selecting informative slices from the CT series. Slice selection is critical to construct informative 3D volumes. Selection eliminates the effect of irrelevant slices on performance and reduces computational time. However, this issue was not inspected, to our knowledge, in the literature [15; 20; 22]. Despite the existing shortcomings, the employment of ML and deep learning in lung cancer decision support systems show substantial prospect in various applications [23]. Such potential encourages further developments that consider the issues and limitations in current studies in attempt to improve the tumor phenotyping and staging performance. Therefore, this study aims to tackle the existing described issues through our proposed framework. It provides an automatic threshold-based segmentation approach, a semisupervised slice selection approach and a newly proposed relatively lightweight 3D CNN architecture.