Tropical Convolutional Neural Networks (TCNNs) Based Methods for Breast Cancer Diagnosis (original) (raw)
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A comparative analysis of convolutional neural networks for breast cancer prediction
International Journal of Electrical and Computer Engineering (IJECE), 2024
Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for subsequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.
Breast Cancer Classification Using Deep Convolutional Neural Networks
FUOYE Journal of Engineering and Technology
Breast cancer remains the primary causes of death for women and much effort has been depleted in the form of screening series for prevention. Given the exponential growth in the number of mammograms collected, computer-assisted diagnosis has become a necessity. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. In this context, the use of automatic image processing techniques resulting from deep learning denotes a promising avenue for assisting in the diagnosis of breast cancer. In this paper, an android software for breast cancer classification using deep learning approach based on a Convolutional Neural Network (CNN) was developed. The software aims to classify the breast tumors to benign or malignant. Experimental results on histopathological images using the BreakHis dataset shows that the DenseNet CNN model achieved high processing performances wit...
A Convolutional Neural Network based Classification Approach for Breast Cancer Detection
Thousands of women worldwide are diagnosed with breast cancer yearly, which may be fatal if not treated. The diagnosis of the condition may take years, by which time the patient has little choice except to have the affected breast removed. Early diagnosis and treatments are the best ways to stop this disease's spread. In this study, the authors presented a Computer Aided Diagnosis (CAD) system to assist in breast cancer diagnosis. The study uses the Wisconsin breast cancer dataset to classify benign and malignant data. For the classification, three pre-trained Deep learning algorithms: Convolutional Neural Network (CNN), Long S hort-Term Memory (LS TM), Multilayer Perceptron (MLP), were used. A novel CNN model that exceeds the performance efficiency of three pre-trained models and requires minimal compilation time is proposed. A number of evaluation matrices are used to analyze the models' classification abilities. Upon closer inspection, it has been established that the proposed CNN model outperforms CNN, LS TM, and MLP models with validation accuracy of 97.85%. CNN and LS TM performed with accuracies of 94.12% with the Adagrad optimizer and 93.5% with the Adam optimizer, respectively. Furthermore, MLP performance with 92.44% accuracy using the Adam optimizer. The proposed CNN model achieves the lowest Loss value and compilation time. In addition, the models' recall value, precision, and f1-score are computed to pick out the most effective model for diagnosing breast cancer on numeric data.
Journal of Computing & Biomedical Informatics
World’s second most occurring cancer is breast cancer. Prediction of disease is one of the most challenging tasks and there are many factors that effect this type of diagnosis like the ability of visual perception. This paper proposed a Convolutional Neural Network (CNN) based proper method for analyzing the earliest signs of breast cancer with the help of mammogram images. The main goal of proposed system is to identify the disease of breast cancer at early stages. Due to this reason, Mammographic image analysis society (MIAS) dataset is used. There are three hundred & twenty two (322) mammograms in the dataset, with 209 images of normal breasts and 133 images of abnormal breasts. While abnormal breasts are further classified as benign (62 images) and malignant (51 images). To implement this system, python library Keras and Tensor Flow libraries are used along with deep learning model CNN. Convolutional Neural Network (CNN) has been shown to be effective in detecting breast cancer ...
Breast Cancer Diagnosis in Women Using Neural Networks and Deep Learning
Journal of ICT Research and Applications
Breast cancer is a deadly disease affecting women around the world. It can spread rapidly into other parts of the body, causing untimely death when undetected due to rapid growth and division of cells in the breast. Early diagnosis of this disease tends to increase the survival rate of women suffering from the disease. The use of technology to detect breast cancer in women has been explored over the years. A major drawback of most research in this area is low accuracy in the detection rate of breast cancer in women. This is partly due to the availability of few data sets to train classifiers and the lack of efficient algorithms that achieve optimal results. This research aimed to develop a model that uses a machine learning approach (convolution neural network) to detect breast cancer in women with significantly high accuracy. In this paper, a model was developed using 569 mammograms of various breasts diagnosed with benign and maligned cancers. The model achieved an accuracy of 98....
Analysis of Mammography for Identifying Cancer Cells using Convolution Neural Networks
Breast cancer is the largest causes of women's death. it is the most commonly diagnosed cancer worldwide. the main modules in breast cancer prediction are data collection data pre-processing feature selection and classification.it is important to detect breast cancer as early as possible. The chances of survival of the patient is high advanced classification techniques and artificial intelligence methods has largely been used for breast classification.in the first part we will be reviewing our breast cancer histology image dataset and we will split the python script into three sets those are a training set, a validation set, a testing set. Next we will use keras libraries to define a convolutional neural network.
Detecting breast cancer using artificial intelligence: Convolutional neural network
Technology and Health Care, 2021
BACKGROUND: One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist's efficiency to visually inspect the appearances of bio-markers on the invasive tumor tissue section. Lately, deep learning techniques have radically enriched the ability of computers to identify objects in images fostering the prospect for fully automated computer-aided diagnosis. Given the noticeable role of nuclear structure in cancer detection, AI's pattern recognizing ability can expedite the diagnostic process. OBJECTIVE: In this study, we propose and implement an image classification technique to identify breast cancer. METHODS: We implement the convolutional neural network (CNN) on breast cancer image data set to identify invasive ductal carcinoma (IDC). RESULT: The proposed CNN model after data augmentation yielded 78.4% classification accuracy. 16% of IDC (−) were predicted incorrectly (false negative) whereas 25% of IDC (+) were predicted incorrectly (false positive). CONCLUSION: The results achieved by the proposed approach have shown that it is feasible to employ a convolutional neural network particularly for breast cancer classification tasks. However, a common problem in any artificial intelligence algorithm is its dependence on the data set. Therefore, the performance of the proposed model might not be generalized.