Cancer Identification

 Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide; thus, its early diagnosis has a significant impact on reducing mortality. However, it is often difficult to dia

Project Title

Cancer Identification

Project Area of Specialization

Artificial Intelligence

Project Summary

 Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide; thus, its early diagnosis has a significant impact on reducing mortality. However, it is often difficult to diagnose breast abnormalities. Different tools such as mammography, ultrasound, and thermography have been developed to screen breast cancer. In this way, the computer helps radiologists identify chest abnormalities more efficiently using image processing and artificial intelligence (AI) tools.

The deep learning algorithm used to identify and outline regions with high likelihood of containing tumor. Algorithm-assisted pathologists demonstrated higher accuracy than either the algorithm or the pathologist alone. Among AI technologies, deep learning has demonstrated strong performance in many automated image-recognition applications. several deep learning– based algorithms have been developed for the detection of breast cancer metastases in lymph nodes as well as for other applications in pathology. Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses.

They include complex preprocessing pipeline including stain normalization, nucleus detection, and region of interest segmentation. This is mainly due to the heterogeneous structure of histopathology images.Convolution Neural Networks (CNN) can achieve higher recognition rates than hand-crafted feature descriptors, but the price to pay is an increase in complexity to develop the system, requiring longer training time and specific expertise to fine-tune the architecture of the CNN.

In this work, we will combine deep learning with transfer learning by means of a supervised layer-based feature transference method.In this method, a deep classifier is obtained (pretrained and fine-tuned) using data from a source problem and reused (partially or not) in the deep classifier for the target problem.By partially, we mean that one can transfer all or part of the source model features (layers) to the target model. In this way, we are transferring knowledge acquired with the source to help in solving the target. It is expected that the TL process supplies the target classifier with an initial set of weights that is a better starting point than the traditional random initialization, providing improved performance (positive transference) over the baseline (by contrast, negative transference occurs when the baseline classifier performs better than the TL classifier).

Project Objectives

Breast cancer is one of the leading causes of death for women globally. According to the World Health Organization (WHO), the number of cancer cases expected in 2025 will be 19.3 million cases. In Egypt, cancer is an increasing problem and especially breast cancer. There are two types of breast cancers which must be a doctor distinguish in the time of diagnosis and this identification is very important for starting treatment process and Prognosis the time period of patients. These two types are Benign breast lump or non-cancerous and  Malignant breast lump or cancerous. Our objective i.e Breast Cancer Diagnosis is recognizing benign from malignant breast cancers and lumps and Breast Cancer Prognosis predicts the high risk people in aspect of breast cancer or predicting recurrence of cancers after treatment or removing their cancers. It is important to detect breast cancer as early as possible. A new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumours in breast mammography images.

In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation. Recently research shows that the accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques.

Project Implementation Method

Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object And speech recognition. Machine learning can help medical professionals to diagnose the disease with more accuracy. In this research we will identify the breast cancer using deep learning techniques.

An online dataset of mammograms will be used for processing and identification of breast cancer. We will use the Digital Database for Screening Mammography .First image processing will be done on the dataset to increase contrast and noise reduction. Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. CCN (convolution neural layer) Architecture will be used to extract the features from the mammogram to identify the defected area using Matlab 2017.

 A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. AlexNet is a convolutional neural network that is trained on more than a million images from the ImageNet database  .ALEXNET will be used in this research. Alexnet is a convolution Neural layer Architecture, AlexNet has five convolution layers, three pooling layers, and two fully connected layers.

Mammograms Dataset ? Image Processing ? Feature Extraction ? Identification Using ML ? CNN Training

In this work, we will combine deep learning with transfer learning by means of a supervised layer-based feature transference method.After extracting the identification will be done on the basis Calcifications, Macrocalcifications, and mass, Lumps, density and cyst. Machine learning techniques with ML classifier will be used in this work for identification. Artificial Neural Network composed of simple elements that are inspired by biological neuron operates in parallel. We train neural network to perform specific function by adjusting weights between elements.

Benefits of the Project

Computerized diagnosis of breast cancer has a number of great advantages; thus, it is constantly being used by radiologists and its impact on the field of medicine is clear. Computer-assisted methods increase diagnosis accuracy by reducing false positives. Advantages of using developed systems for diagnosing breast cancer include:

• helping radiologists in the process of interpretation and screening as a second interpreter after the radiologist;
• reducing the number of false positives, which will eliminate the need for unnecessary biopsy and lead to cost savings; and
• reducing the time of the patient’s examination by reviewing and reporting the findings in a few seconds.

So far, creative methods have been developed to diagnose and classify breast cancer; however, none of the methods has been able to accurately classify all cancer cases. In recent years, application of the AI techniques along with image processing has yielded significant results. This article examined these techniques, which used image processing to diagnose breast cancer. The results of the study showed that the SVM had the highest accuracy in cancer diagnosis.

This method is considered as a very efficient method when it comes to solving classification problems with noise data. Two of the main reasons behind the reliability of the SVM classification engine include 1) choosing an optimal subset of context properties for learning and 2) appropriately regulating page parameters using the v-fold cross-validation approach.

The most significant benefits of algorithm assistance observed in this study were for efficiency, with a time savings of 19% for negative cases and 52% for micro metastases. In addition to the time benefits associated with the assistance tool, we also observed independent, statistically significant time differences between the first and second sessions of this cross over study. One likely possibility is that the study participants became more familiar with all aspects of the viewer interface and the specific task such that their review was shorter. This is also consistent with the efficiency gains reported with increased digital pathology experience.

Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity.In breast cancer histopathology image analysis, convolutional neural networks are used for region of interest detection, segmentation and also for mitosis detection.

Technical Details of Final Deliverable

The aim of this project  is to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set.

Although the diagnosis of breast cancer can be highly accurate, it is not necessarily the same as the results obtained in other sets of images. Therefore, future research can be done to improve the system’s performance and validate it by conducting tests on a larger set of images. In addition, the following can be suggested as future work: 1) setting SVM parameters through using a genetic algorithm: traditionally, feature selection and parameter optimization are performed independently; independently performing these two problems may result in a loss of information related to the classification process; motivated by these views, the trend in recent years is to simultaneously select feature subsets and optimize parameters of SVM genetic algorithms have the potential to generate both the optimal feature subset and SVM parameters simultaneously; and the most widely used genetic algorithm is proposed by Huang et al and 2) evaluating other texture approaches: texture extraction methods are classified into three main categories: structural, statistical, and spectral.

Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful treatment and survival. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. However, traditional feature extraction methods can only extract some low-level features of images, and prior knowledge is necessary to select useful features, which can be greatly affected by humans. Deep learning techniques can extract high-level abstract features from images automatically. Therefore, we will introduce it to analyze histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks.

As a consequence, classified histopathological images of breast cancer from BreaKHis using a variation of the AlexNet convolutional neural network that improved classification accuracy by 4–6%.  proposed to classify breast cancer histopathological images independently of their magnifications using CNN (convolutional neural networks). They proposed two different architectures: the single task CNN used to predict malignancy, and the multi-task CNN used to predict both malignancy and image magnification level simultaneously. Evaluations were carried out on the BreaKHis dataset, and the experimental results were competitive with the state-of-the-art results obtained from traditional machine learning methods.

Final Deliverable of the Project

HW/SW integrated system

Type of Industry

Medical , Health

Technologies

NeuroTech, Others

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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