Backgroud: Lung cancer is the most common cancer in terms of both incidence and
mortality. Where medical science is playing its role to overcome this deadly disease, new advancements's
and research areis also going on in computer science especially in the domain of image
processing to support doctors and radiologists to tackle it. Developments are going on in image
processing and CAD evaluation for applications that include cancer screening, diagnosis, and image-
guided intervention, and treatment.
Methods: The most efficient way to stop the cancer is to detect and diagnose it at an early stage.
Most of the existing CAD systems monitor the growth of lung nodules over a period of time, which
is not possible at the early stage of lung cancer. In case of lung cancer treatment in Pakistan, even
if the cancer is at later stages, there are no such archives in which the history of patient is maintained.
So in that case, it becomes extremely important to develop such system which detects lung
cancer at its early stage without depending on the requirement of patient history. Secondly, majority
of the CAD systems require training prior to use and after the training, they still cannot’t produce
satisfactory results. And the last point is most of the scanners comes with built-in software
and most of the scanners do notesn’t support third party software's.
Results: In this study, a CAD system is proposed for the detection of malignant nodules through
traditional image processing techniques fused with the techniques used by radiologists. The system
goes through three main phases; pre-processing, segmentation and 3D reconstruction. In the first
phase, pre-processing techniques weare used to remove unwanted information and enhance the image
for further processing. During the second phase, the nodule wais detected and localized and in
the last phase, 3D reconstruction of the nodule is performed for better visualization that supports
the radiologist and the surgeon/doctor. At By the end of the study, we have discussed the performance
of our CAD system on LIDC dataset.
Discussion: This dataset consists of 1018 cases from which we randomly selected 340 cases and
compared the results of our methodology using four different scenarios against studies which have
used Artificial Neural Networks (ANN) and Support Vector Machine (SVM).
Conclusion: The methodology used in this study, clearly outperforms in two out of the four scenarios
when compared to ANN and SVM.