Breast cancer detection using Resnet50
ABSTARCT :
Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods.
Previous research has shown that the use of the logistic regression, svm and random forest algorithms in breast cancer detection still does not achieve high accuracy.
This study aims to improve the accuracy of breast cancer detection using the Inception ResNet v2 transfer learning method and data augmentation.
The data is divided into training, validation and testing data consisting of 3 classes, namely Benign, Malignant and Normal. The augmentation process includes rotation, zoom, and rescale.
The model trained using CNN and Inception ResNet v2 showed good performance by producing the highest accuracy of 89.72% in the training data evaluation data and getting 90% accuracy in the prediction test stage with data testing.
This study shows that the combination of data augmentation and the Inception ResNet v2 architecture can improve the accuracy of breast cancer detection in CNN models.
EXISTING SYSTEM :
The performance classification accuracy has been encouraging comparison with various existing classifiers implemented in the literature. As expected the proposed model classifies diagnosis cases into either cancerous or non cancerous with high amount of accuracy.
Even if technology has developed, still lots of people are facing many issues with modern age diseases. Breast cancer has become one of the most common deadliest diseases rising over days among all countries in world.
Ratio of this disease increases due to lack of awareness and late identification. Our result reveals ANN (machine learning) plays measure factor for detection of cancer diagnosis to save the human life from the dangerous disease.
DISADVANTAGE :
Previous researches reveal the increasing threats of breast cancer which drive to delve into unsolved and problematic issue, which is the main reason for the work reported in this paper. In the early stage of the breast cancer it appear as a malevolent lump and later stage it growths uncontrolled and variable manner.
By the help of the feature selector the datasets is free from ambiguity and dimension of data is also reduced. It solves the over fitting problem of dataset
The no of stages varies within the problem description of BPNN (Back Propagation Neural Network) is a gradient descent algorithm used in various domains
Previous researches reveal the increasing threats of breast cancer which drive to delve into unsolved and problematic issue, which is the main reason for the work reported in this paper. In the early stage of the breast cancer it appear as a malevolent lump and later stage it growths uncontrolled and variable manner.
By the help of the feature selector the datasets is free from ambiguity and dimension of data is also reduced. It solves the over fitting problem of dataset
The no of stages varies within the problem description of BPNN (Back Propagation Neural Network) is a gradient descent algorithm used in various domains
PROPOSED SYSTEM :
The proposed system of the InceptionResNet-v2 architecture method is its ability to recognize objects with high accuracy and handle unstructured data by extracting relevant features from images using convolution.
Where Convolution is the act of image extraction to obtain a model in the form of a kernel matrix. In this process, filtering is carried out that shifts with a certain "step value" on an image input.
Or a process to look at the value of a parameter that determines how much the filter shifts in the input image. Furthermore, the results of the convolution become inputs for the fully connected parts for the classification process.
ADVANTAGE :
Some cases CAD (Computer Aided Tool) is also used by the physicians.
Various machine learning methods are used to detect whether the cancer gene is benign (or) malignant.
Then reduced data are used for ANN and classification has been performed
For classification radial basic function (RBF), General Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) were used.
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