Liver Tumor detection Using Deep Learning Techniques

Project Code :TCMAPY851


The primary goal of this project is to detect the presence of the liver tumor.


Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumour when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumour. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumour is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumours from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumour. The best results that we obtained within this research reached a good accuracy, and good precision  , which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.

Keywords: Medicinal, deep learning, Mobile net model, CNN, minutiae.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


H/W Specifications:

Processor                         :  I5/Intel Processor

RAM                                     :  8GB (min)

Hard Disk                           :  128 GB

S/W Specifications:

Operating System              :   Windows 10

Server-side Script              :   Python 3.6

IDE                 :   PyCharm, Jupyter notebook

Libraries Used :   Numpy, IO, OS, Django, Keras, pandas, tensorflow

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