Mammographic Image Analysis Society Dataset Research Articles

An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset.

Generic hybrid model for breast cancer mammography image classification using EfficientNetB2

Breast cancer is a global health issue that necessitates precise classification for early detection and effective treatment. In recent years, pre-trained models have shown great potential in the field of medical image classification, including breast cancer classification. These models have been trained on extensive datasets, and they possess the ability to capture intricate features and patterns within medical images, facilitating accurate classification. However, some of the models are non-generic. They can be sensitive to dataset biases, leading to over fitting on specific patterns present in the training data, and they equally struggle to handle data from different distributions. In this work, we proposed a generic hybrid model for image classification. The features were extracted from two datasets: the mammographic image analysis society (MIAS) and the INbreast dataset, respectively, through the pre trained EfficientNetB2 architecture. However, three classifiers were used in the image classification of the extracted features: MGSVM, CUBIC SVM, and XGBOOST. Eight evaluation metrics were selected to assess the performance of the proposed models. These metrics include accuracy, precision, F1-score, AUC, sensitivity, false negative rate (FNR), Kappa score, and time complexity. Experimental results show that the hybrid of EfficientNetB2 and the MGSVM classifier is more generic and efficient for breast cancer diagnosis and classification. It exhibits a strong performance when classifying mammography breast images from both datasets, achieving impressive metrics such as an overall accuracy of 99.47%, a sensitivity rate of 99.31%, precision of 99.44%, F1-score of 99.44%, AUC of 99.44%, a low FNR (False Negative Rate) of 0.007, a kappa score of 0.98, and a manageable time complexity of 231.44 seconds on the MIAS Dataset.

English

Breast cancer is among the top causes of fatalities related to cancer in females. Radiologists commonly use mammogram images to detect breast tumors in their early stages. However, mammography can produce low-contrast images, making it difficult and time-consuming to segment abnormal regions. Deep convolutional neural networks (CNNs) are commonly used for image evaluations. This study used deep CNN models to develop a computer-aided diagnostic (CAD) system for feature extraction and classification. The proposed approach consists of three phases. In the first phase, a shallow, deep CNN model comprising five convolutional layers, five max-pooling layers, one batch normalization layer, and one dropout layer was developed and used to extract recombined images and novel features. In the second phase, the Inception-v3 model was used for label smoothing and classification due to its multiple filters with different sizes. In the third phase, features were extracted using shallow, deep CNN and Inception-v3 models. The Infallible Euclidean distance-based nonlinear dimensionality reduction approach was used to minimize dimensionality. Finally, the Gini-index-based C4.5 decision tree was used for the binary classification of mammogram images from the Digital Database for Screening Mammography (DDSM) + Curated Breast Imaging Subset of DDSM (CBIS-DDSM) and Mammographic Image Analysis Society (MIAS) datasets. The proposed hybrid shallow, deep CNN and Inception-v3 model achieved 99.52% accuracy, a 96% AUC on the DDSM + CBISDDSM dataset, and an accuracy of 97.53% and an AUC value of 97% on the MIAS dataset. Compared with other cutting-edge CAD systems, the proposed hybrid approach achieved higher accuracy by combining in-depth features across both datasets.

Classifications of benign and malignant mammogram images using Gabor‐modified CNN architecture

AbstractBreast cancer is one of the leading life killing cancers in women patients around the world. Digital mammogram is used to detect and segment the abnormal mass portions in breast region. In this article, the breast cancer regions are detected and segmented using the Gabor transform based Convolutional Neural Networks (CNN) classification approach. This proposed breast cancer detection method consist of the following modules Kirsch's edge detector, Gabor transform, CNN classification and Segmentation. The cancer pixels and healthy pixels are differentiated in edge pixels. Hence, the Kirsch's edge detector is used to detect the fine edge boundary pixelsthe in source mammogram image. Then, the spatial edge detected mammogram image is converted into multi resolution mammogram image using Gabor transform. This transformed multi resolution mammogram image is classified into Normal, Benign or Malignant in this article using the proposed CNN architecture. The developed CNN structure extracts more complex feature maps from their internal layers to improve the classification rate. The conventional and proposed CNN structure is differed with respect to the internal layers. The proposed CNN structure is optimized by reducing the usage of the internals and increases Convolutional Filters (CF) in each Convolutional Layer during utilization design process. After classification process over, the cancer regions in the abnormal mammogram images are segmented using morphological functions. The proposed breast cancer detection is evaluated on Mammographic Image Analysis Society (MIAS) dataset and their cancer region segmentation results are 98.4% of sensitivity, 98.9% of specificity and 99.2% of cancer region segmentation accuracy. The average Detection Rate (DR) of the proposed breast cancer detection method is about 98.3% on the set of mammogramthe images from MIAS dataset. These simulation results are compared with other state‐of‐the art methods and cross verified by the k‐fold verification algorithm.

Breast cancer detection and diagnosis using hybrid deep learning architecture

The detection of cancer regions in the breast is important to save the women patient’s life. In this paper, mammogram image is classified into normal, benign, and malignant using the proposed hybrid Convolutional Neural Networks (CNN) architecture. The proposed system consists of a radon transform, data augmentation module, and hybrid CNN architecture. The radon transform transforms each spatial pixel in the source mammogram image into a time–frequency variation image. This image is data augmented to construct a new dataset from the existing dataset to improve the breast cancer detection rate. The data augmented images are classified into three different cases using the proposed hybrid CNN architecture. Further, a mathematical morphological-based segmentation algorithm is used to segment the cancer pixels. In this article, Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets for mammogram image analysis are used to estimate the performance efficiency of the developed deep learning architecture. The developed CNN architecture provides 97.91% Se, 97.83% Sp, 98.44% Acc, and 98.57% JI on the mammogram images available in the DDSM dataset. The developed CNN architecture provides 98% Se, 98.66% Sp, 99.17% Acc, and 98.07% JI on the mammogram images available in the MIAS dataset. For both open access datasets, the experimental results are compared to recent similar works. From the extensive analysis of experimental results of the proposed method, the methodologies presented in this article clearly segment the boundary of the cancer region in an abnormal mammogram image.

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.

Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

AbstractBreast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer‐aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization‐crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and adaptive genetic algorithm, teaching learning‐based whale optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, and grasshopper optimization algorithm. From the results obtained, the proposed grasshopper optimization‐crow search algorithm with the multilayer perceptron method outperforms the comparative models in terms of classification accuracy (97.1%), sensitivity (98%), and specificity (95.4%) for the mammographic image analysis society dataset.

Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm.

In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.

Detection of Architectural Distorted Region in Mammogram Images Using Hybrid Classification Approaches

The architectural distorted regions in mammogram images are detected and segmented using computer aided hybrid classification approach in this paper. The main importance of this research work is to provide a computer aided methodology for screening the distorted regions in mammogram images. In present approach, the classification accuracy of the conventional methods is not suitable for further diagnosis process such as malignant and benign. Hence, the main objective of this paper is to develop an efficient architectural region detection method using soft computing method with high classification accuracy for further diagnosis purpose. This proposed method has two stages of the proposed flow as architectural distorted detected mammogram image and segmentation of architectural distorted regions in mammogram images. The first stage of this proposed method uses Random Forest (RF) classification method which classifies the source mammogram image into either normal or abnormal. In second stage of the proposed method, the abnormal image is further classified into either Benign or Malignant using Adaptive Neuro Fuzzy Inference System (ANFIS) classification approach. The proposed methodology for architectural distorted region detection is tested on the publicly available mammogram datasets Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) respectively. In this paper, the mammogram images from MIAS dataset are grouped into normal case (156 images), benign case (122 images) and malignant case (98 images). The mammogram images from DDSM dataset are grouped into normal case (144 images), benign case (112 images) and malignant case (145 images). The overall average detection rate of the proposed system on the mammogram images in MIAS dataset is about 98.7%. The overall average detection rate of the proposed system on the mammogram images in DDSM dataset is about 98.3%. The extensive simulations are carried out on the mammogram images which are obtained from these dataset and the results are compared with stated of art methods.

A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm.

Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.