Breast imaging technique called mammography has gained bigger attention among the researchers for the diagnosis of breast malignancy in the woman. Mammogram screening is the most effective procedure to visualize various potential problems in the breast. The two most common features connected with breast tumors are mass lesions and microcalcification. The collection of suitable image preprocessing, segmentation, feature extraction, selection and prediction algorithms play an essential role in the accurate detection and classification of cancer on mammograms. Classification techniques estimate unlabeled datasets class labeling depending on its similarity to the pattern learned. The Glowworm Swarm Optimization(GSO) algorithm is ideal for finding several solutions, and dissimilar or equivalent objective function values at the same time. This feature of GSO is useful for optimizing the feature set obtained from multiscale feature extraction procedures. Poor performance in generalization is the issue that arises due to the unconditioned output matrix of the hidden stage of the ELM classifier. The optimization algorithms will address this matter because of their global search capabilities. This article suggests ELM with the Fruitfly Optimization Algorithm (ELM‐FOA) along with GSO to regulate the input weight to achieve maximal performance at the hidden node of the ELM. The testing precision and sensitivity of GSO‐ELM‐FOA are 100% and 97.91%, respectively. The system developed will detect the calcifications and tumors with an accuracy of 99.15%.
Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer-aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based segmentation applied to a localized area.In the second stage of the CAD method, the extraction of the feature is carried out using three distinct wavelet families with decomposition level at 4 and 6. The ANN, SVM, and ELM classifiers are used in the final stage to enable accurate classification. This article proposes ELM with the Grasshopper Optimization Algorithm (ELM-GOA) to adjust the weight between the input and hidden layer to obtain maximum performance at the middle layer. This method adopts mammogram enhancement, optimum image segmentation, wavelet-based feature extraction, and grasshopper optimization algorithm based ELM to ameliorating the accuracy and reducing the computational cost.The result shows that ELM-GOA has precision and sensitivity of 100% and 98% respectively. The CAD system can identify tumors with 99.33 % accuracy.
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