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Posted: May 4th, 2022

Early Detection of Lung Cancer based on Medical Image Processing Expert System and Data Analytics

Early Detection of Lung Cancer based on Medical Image Processing Expert System and Data Analytics

Lung cancer is one of the most deadly diseases in the world, causing millions of deaths every year. It is often diagnosed at a late stage, when the treatment options are limited and the prognosis is poor. Therefore, early detection of lung cancer is crucial for improving the survival rate and the quality of life of the patients.

One of the main challenges in early detection of lung cancer is to identify and classify small nodules in the lung tissue, which may indicate the presence of cancerous cells. These nodules are usually detected by computed tomography (CT) scans, which provide high-resolution images of the lung anatomy. However, analyzing these images manually is time-consuming, error-prone and subjective. Moreover, some nodules may be missed or misclassified by human experts, leading to false negatives or false positives.

To overcome these limitations, many researchers have proposed to use medical image processing expert systems and data analytics techniques to automate the detection and diagnosis of lung cancer. These techniques use artificial intelligence (AI) algorithms to extract relevant features from the CT images, such as shape, size, texture and location of the nodules, and to classify them into benign or malignant categories based on their characteristics. These algorithms can also learn from previous cases and improve their performance over time.

In this blog post, we will review some of the recent advances in this field, focusing on three aspects: object detection, segmentation and classification. We will also discuss some of the challenges and future directions for this research area.

Object Detection

Object detection is the task of locating and identifying objects of interest in an image. In the context of lung cancer screening, object detection aims to find and label all the nodules in a CT scan. This is a difficult problem, because nodules can vary in size, shape, density and contrast. Moreover, nodules can be occluded by other structures in the lung, such as blood vessels or bronchi.

One of the most popular methods for object detection is based on deep learning, which is a branch of AI that uses neural networks to learn from data. Neural networks are composed of layers of artificial neurons that can perform complex computations and adapt their weights according to the input and output data. Deep learning has achieved remarkable results in various domains, such as computer vision, natural language processing and speech recognition.

One of the most successful deep learning models for object detection is called YOLO (You Only Look Once), which was first introduced by Redmon et al. [1] in 2016. YOLO is a single-stage detector that divides the input image into a grid of cells and predicts for each cell a set of bounding boxes and confidence scores for different object classes. YOLO is fast and accurate, but it has some limitations, such as low recall for small objects and difficulty in handling overlapping objects.

To address these issues, several improvements have been made to YOLO over the years. For example, YOLO v7 [2] is a recent version that uses an enhanced backbone network (Darknet-53) with residual connections and a feature pyramid network (FPN) to extract multi-scale features from different levels of the network. YOLO v7 also uses anchor boxes with different shapes and sizes to better fit the objects in the image.

YOLO v7 has been applied to lung nodule detection by Mammeri et al. [3] in 2023. They used the LIDC-IDRI dataset [4], which contains 1018 CT scans with annotations from four radiologists for 2669 nodules ranging from 3mm to 30mm in diameter. They evaluated the impact of different types of input images on nodule detection: whole images (without preprocessing or segmentation), lung segmented images (only the lung area extracted from the whole images) and preprocessed images (with some filtering methods applied on the whole images). They found that using whole images resulted in the best performance, achieving a mean average precision (mAP) of 81.28% for nodule detection.


Segmentation is the task of dividing an image into regions that correspond to different objects or parts of objects. In the context of lung cancer screening, segmentation aims to separate


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