###### Abstract  
Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, _DETECT-LC_ learning, _DETECT-LC_ handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE _Net_ architecture is introduced as part of _DETECT-LC_ for voxel-based classification. _DETECT-LC_ offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, _DETECT-LC_ presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. _DETECT-LC_ performance is assessed through a range of experiments, in which _DETECT-LC_ attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, _DETECT-LC_ average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14.  
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the developments in diagnostics and lung cancer staging [4] fields have led to increased survival in early stage cancers. Lung cancer staging is crucial for therapy planning and thus has a significant positive prognostic effect [4]. Medical imaging is one of the fundamental steps for effective staging. Different modalities are available for lung imaging and screening, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Positron Emission Tomography (PET) scans. However, CT remains the standard imaging modality for preoperative staging. CT imaging has the advantages of lower cost and shorter examination time, together with its comparable performance [5] relative to other imaging modalities. Recently, machine learning (ML) and deep learning (DL) have shown epic potential in enhancing clinical decision making [6]. In oncology, ML and DL are applied in the diagnosis and staging phase, as well as the follow-up and treatment evaluation phase [7]. The immense advances in ML techniques and the availability of computationally powerful devices enable the effective processing of volumetric imaging data for diagnostic and staging purposes [8].Volumetric data, for CT scans, is composed of two dimensional (2D) stacked images, that gives better representation of the lung. Such enhanced representation is generally related to improved performance [9]. However, the election of meaningful 2D slices for 3D volume reconstruction, has a considerable impact on the performance. In this paper, _DETECT-LC_ pipeline is proposed for Non-small cell lung cancer (NSCLC) diagnosis and staging. The pipeline incorporates:  
* A simple automatic segmentation technique using hounsfield unit values and subsequent thresholding with acceptable performance. * A semi-supervised 2D slice election approach, which depends on textural features. The adopted clustering-based approach eliminates the need for extensive human intervention, allowing for faster processing. * A simple 3D DL network architecture ALT-CNN-DENSE _Net_ for effective lung cancer staging and tumor phenotyping. The performance of the proposed network surpasses the performance of the state of the art. This paper is organised as follows: a brief background on lung cancer staging standard will be given in the next section.