AU2020101122A4 - Cnn model based approach towards breast cancer features detection from ultra sound images - Google Patents
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Abstract
CNN MODEL BASED APPROACH TOWARDS BREAST CANCER
FEATURES DETECTION FROM ULTRA SOUND IMAGES
ABSTRACT
Breast cancer is the serious cause of death for women globally. The Breast cancer cells divides
faster to the lymph nodes and even leads damage to other parts of the body. Detection of breast
cancer as early as possible is very important. Ultra sound is one of the methods to detect breast
cancer in earlier stage and its advantage is that, it doesn't have any radiation. Machine Learning
plays a very important role in medical imaging research. Here, the deep learning technique is used
to classify the breast cancer. The Deep learning methods have great capability in medical research.
The approach of using image enhancement techniques is to improve the diagnostic accuracy of
medical image technologies like ultrasound imaging. Computer machine learning advanced
technologies, for example, Convolutional Neural Networks (CNNs) have risen as a viable tool in
medical image analysis for the detection and classification of disease in various way progressively.
Image processing and machine learning techniques helps to diagnose the various disorder in prior
itself by using the breast ultrasound image. The breast ultrasound image is used to recognize the
cancer in early stages. Microwave sensor plays a major role in microwave imaging system. The
main aim of this title is to detect the disease present in the breast images using machine learning
techniques. Results show that the neural network is superior to the other techniques for
classification.
1| P a g e
CNN MODEL BASED APPROACH TOWARDS BREAST CANCER
FEATURES DETECTION FROM ULTRA SOUND IMAGES
DIAGRAM
Figure :reC- IageuagC Feature
process FaeSegmetation Eaction
Bcgound and
Musol RenevalFeature
Seletion
Necrmion Training convolutional
DecisionNeural Network for
Abnomnal - classificatin
Testinig
B3emga Malgumat
Tumor Tumor
Figure 1: Breast Cancer Feature Detection using CNN Model
1 |P a g e
Description
Figure :reC- IageuagC Feature process FaeSegmetation Eaction
Bcgound and Musol RenevalFeature Seletion
Necrmion Training convolutional DecisionNeural Network for Abnomnal - classificatin Testinig
B3emga Malgumat Tumor Tumor
Figure 1: Breast Cancer Feature Detection using CNN Model
1 |P a g e
Cancer is characterized by the growth of abnormal cells that destroys the healthy tissues or cells in the body. Breast Cancer can take place for both men and women. But it is most common disease in women when compared to men. The most common cancer which is diagnosed in women is breast cancer. The survival rates for breast cancer have been increased. Breast cancer occurs due to abnormal growth of the breast cells. The abnormal cells divide more faster within a short period than a healthy cell and it may spread through out the breast or to other parts of the body. If the cell growth is out of control, the tumor is called benign (non-cancerous). If the cell growth is out of control and if it is abnormal and does not function like normal body's cell, the tumor is called as malignant (cancerous). There are different types of breast cancer: 1. Ductal Carcinoma, 2. Lobular Carcinoma. Most probably the breast cancer initiates within the cells in invasive ductal carcinoma or milk-producing ducts and it is the most common type. It may begin from glandular tissue which is also known as lobules or invasive lobular carcinoma or also it may initiate from other cells or tissue which is present in the breast. Invasive and Non-invasive breast cancer is a type of breast cancer. When the abnormal cells break out from the lobules and ducts, it enters the nearby tissue and this is known as invasive breast cancer. There is chance to increase the spreading of cancer to other parts of the body. If the cancer remains inside the place of origin and if it is not spread to the other nodes, then it is known as noninvasive breast cancer.
The symptoms of breast cancer includes that thickened tissue in the breast that will be different than the surrounding tissue , lump in the breast or an armpit, redness of the skin, rashes around the nipple, discharge of blood from a nipple, sunken or inverted nipple, changes in the size and shape of the breast, peeling or scaling on the breast or nipple, breast pain.
The stage of the cancer is known according to the size of the tumor or growth of the tissue and whether the cancer cells has spread to the lymph nodes or the other parts of the body. The breast cancer is categorized into different stages.
1. Stage 0 - The stage 0 is also known as ductal carcinoma. Stage 0 is known as DCIS. The cells are limited in the ducts and does not enter the surrounding tissues. 2. Stage 1 - i) Stage 1A: In this stage, the tumor is in the range of 2 centimeter wide or less. This is the primary tumor. It does not affect any of the lymph nodes. ii) Stage IB: Small group of cancer cells may present near the lymph nodes or no tumor is present in the breast or the tumor will be lesser than 2cm. 3. Stage 2 - i) Stage 2A: The tumor will be smaller than 2cm across and started spreading to 1-3 nearby lymph nodes or it will be between 2 and 5 cm and does not spread to any lymph nodes. ii) Stage 2B: The Tumor will be between 2 and 5cm and will spread to 1-3 armpit lymph nodes or it can be larger than 5cm and does not spread to any lymph nodes. 4. Stage 3 - i) Stage 3A: The cancer spreads to 4-9 armpit lymph nodes. Tumor can be greater than 5cm and the cancer spreads to 1-3 armpit lymph nodes. ii) Stage 3B: A tumor will enter to the chest wall or skin and does not invade up to 9 lymph nodes. iii) Stage 3C: Cancer is spread to 10 or more armpit lymph nodes. 5. Stage 4 - In this stage, the tumor can be any of the size. The cancer spreads to the other part of the body like bones, liver, lungs or brain.
The Breast cancer can be detected with the help of tests.
Mammogram: The Mammogram is a type of X-ray and the doctors use this method for an initial breast cancer screening. The output will be an image format and it helps the doctor to detect the lumps or any of the abnormalities present in the breast. Sometimes, Mammography shows a doubtful area turns out not to be a cancer.
MRI: Magnetic Resonance Imaging helps the doctor to identify the cancer or other abnormalities. In this method, different images of the breast are combined to help the doctor in order to identify the cancer and abnormalities. MRI is done when the radiologists wants to confirm about the tumor. The main drawback of MRI is that the patient may have allergic or skin infection at the place of injection.
Ultrasound: The Ultrasound uses the sound waves in order to help the doctor to know the difference between the solid mass and a fluid-filled cyst. Breast ultrasound is an imaging technique and with the help of sound waves doctors can view inside the breast. Using this method, the person can know the blood flow condition to all the areas inside the breast. In this method, the trainer moves the transducer on the skin to make the images of the breast. The transducer sends out the sound waves that bounces off the breast tissue and the transducer pick up the bounced sound wave. The sound waves are high pitched to hear. These are taken as images of the internal part of the breast. The Trainer adds another device called doppler probe to the transducer. The probe helps the trainer to hear the sound waves which is sent out by the transducer and the trainer can hear how fast the blood is flowing to the blood vessel and the direction of blood flow. If the flow is not there, sound will not be heard by the trainer. The advantage of ultra sound is that, for pregnant ladies and breast-feeding mother, the ultra sound is safe because it doesn't have any radiation.
Convolutional Neural Networks (CNNs) is a branch of deep learning which is extensively used in image analysis and medical imaging. CNN became successful in complicated image recognition problems. CNNs are used viably to handle complicated computer vision tasks. Convolutional Neural Network is a multi-layer, and feed-forward neural network, that makes use of perceptron's for supervised learning and to analyze the facts and this feed-forward network consists of an input layer, many hidden layers (convolutional layers, normalization layers, pooling layers, and a fully connected layer) and a final multi-label classification layer. It consists of two stages in processing: i) A million of images will go through many iterations of CNN architecture in order to conclude the factors of each layer and this is the time-consuming training stage. ii) Each image in the test dataset is fed into the trained model in order to score and validate some factors and this is the real-time prediction stage. Firstly, an input image will be fed and some significant weights will be assigned. The first convolution layer will be captured in the form of low-level features, the next layers are extract to the higher-level features, and creating a network with a sophisticated evaluation of the images in the dataset.
Tumor is classified as benign and malignant. The main difference between benign and malignant is based on the structure. The benign tumor will be in round or oval shape whereas malignant tumor will be partially round shape and irregular outline and malignant mass will be whiter than any other nearby tissue. The abnormal cells divide more faster within a short period than a healthy cell and it may spread throughout the breast or to other parts of the body. If the cell growth is out of control, the tumor is called benign (non-cancerous). If the cell growth is out of control and if it is abnormal and does not function like normal body's cell, the tumor is called as malignant (cancerous). With the help of neural network technique, the tissue can be identified as whether it is
An overview of the proposed method for the detection of breast cancer architecture is shown in Figure 1. Here, the steps followed in the proposed method are: i) original ultra sound image taken as input, ii) Image Pre-processing (Background removal and muscle removal), iii) Image enhancement), iv) Image segmentation v) Feature Extraction vi) Deep-learning algorithm (CNN classifier) vii) Decision-Making.
The Ultrasound image is taken as input and the input will be in grayscale image. In image pre processing, the noise which is present in the image can be removed using filtering technique. The filtering also avoids the unwanted background noise to detect the abnormality. The major challenge in pre-processing is find the pectoral muscle boundary accurately. Image Enhancement helps to increase the contrast and brightness of the ultra sound image. It helps from poor visibility and its main aim is to improve the quality of the image on the ultrasound image. The spatial domain, frequency domain and combination of spatial and frequency domain are the techniques of the image enhancement. The Segmentation method helps further for the future use i.e. for feature extraction and detection of abnormalities. Image segmentation is the method to segment the images into the various parts in order analyze the abnormalities easily. Thresholding is the technique used to segment the image. The image pixels will be segmented using thresholding algorithm. The Image can also be segmented into regions using region-based segmentation method. The Grayscale image should be converted to binary image using some thresholding technique. Labelling will be done to the objects which is present in binary image and the counting of number of pixels can be done. Using the thresholding value, all other unwanted objects can be removed except the tumor. The convolutional neural network is used for feature extractions. The CNN is a promising method for medical research. The CNN consists of three main layers i) convolutional layer ii) pooling layer iii) fully connected layer. The CNN helps to classify whether the tissue is normal or abnormal. There are two categories with CNN for Breast cancer Detection. i) Identifying the breast cancer patients and ii) Identifying the non breast cancer patients. If the person is having breast cancer, the tumor is indicated as malignant and finds out whether it is invasive or non-invasive. The malignant is cancerous and benign is non-cancerous. The Decision-making results in the classifying the type of disease and the stage of disease.
In biomedical application, microwave imaging plays a major role because of its dielectric properties for different tissues. It can provide functional process of the health condition. Microwave imaging helps to detect or locate techniques and evaluates the hidden object in a structure using electromagnetic in microwave range. The Antenna emits the microwaves and also it detects the shape and location of the tumor from the scattered microwaves. The Ultrasound screening technique using microwaves will be painless, accurate and safer. The drawback of microwave technology is that it is used only for purpose of breast cancer detection. Microwave can pass through the fatty tissue which is the main component of breast but it doesn't pass through the muscle.
Claims (8)
1. The Ultra sound is an imaging technique and it uses the sound waves in order to help the doctor to know the difference between the solid mass and a fluid-filled cyst.
2. Ultrasound technique is also used to identify whether the proper blood flow is there or not in all areas of the breast.
3. The advantage of ultrasound technique is that, it is safe for pregnant ladies and breast feeding women because it doesn't have radiation.
4. The pre-processing algorithms used to eliminate all the noise content in the image.
5. Lesions will be poorly visible or not visible in the original image in some of the situations. This will be visible in the enhanced images. The Enhancement technique is used to improve image brightness and contrast.
6. Image segmentation is used to segment the image pixels or image into regions in order to focus on the tumor and removes the other unwanted objects in the image.
7. For breast cancer diagnosis, deep learning methods shows a promising solution and Convolutional neural network is used for feature extraction.
8. CNN can classify the stage of the disease and what type of disease is present for the patient.
1 Pag e
CNN MODEL BASED APPROACH TOWARDS BREAST CANCER Jun 2020
FEATURES DETECTION FROM ULTRA SOUND IMAGES
DIAGRAM 2020101122
Figure 1: Breast Cancer Feature Detection using CNN Model
1|Page
Figure 2: Schematic of the Breast Scanner
2|Page
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| CN112164462A (en) * | 2020-09-27 | 2021-01-01 | 华南理工大学 | Breast cancer risk assessment method, system, medium and equipment |
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| CN116309585A (en) * | 2023-05-22 | 2023-06-23 | 山东大学 | Method and system for identifying breast ultrasound image target area based on multitask learning |
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| CN112164462A (en) * | 2020-09-27 | 2021-01-01 | 华南理工大学 | Breast cancer risk assessment method, system, medium and equipment |
| CN112164462B (en) * | 2020-09-27 | 2022-05-24 | 华南理工大学 | Breast cancer risk assessment method, system, medium and equipment |
| CN112819715A (en) * | 2021-01-29 | 2021-05-18 | 北京百度网讯科技有限公司 | Data recovery method, network training method, related device and electronic equipment |
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| CN116309585A (en) * | 2023-05-22 | 2023-06-23 | 山东大学 | Method and system for identifying breast ultrasound image target area based on multitask learning |
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