  Early and correct diagnosis is a very important aspect of cancer treatment.
Detection of tumour in Computed Tomography scan is a tedious and tricky task
which requires expert knowledge and a lot of human working hours. As small
human error is present in any work he does, it is possible that a CT scan could
be misdiagnosed causing the patient to become terminal. This paper introduces a
novel fully automated framework which helps to detect and segment tumour, if
present in a lung CT scan series. It also provides useful analysis of the
detected tumour such as its approximate volume, centre location and more. The
framework provides a single click solution which analyses all CT images of a
single patient series in one go. It helps to reduce the work of manually going
through each CT slice and provides quicker and more accurate tumour diagnosis.
It makes use of customized image processing and image segmentation methods, to
detect and segment the prospective tumour region from the CT scan. It then uses
a trained ensemble classifier to correctly classify the segmented region as
being tumour or not. Tumour analysis further computed can then be used to
determine malignity of the tumour. With an accuracy of 98.14%, the implemented
framework can be used in various practical scenarios, capable of eliminating
need of any expert pathologist intervention.
