# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 1 Introduction and Background
Various deep learning methods have been employed to address these challenges, such as utilizing multilayer nonlinear data processing, classification, feature selection, transformation, and structure identification. Among these architectures, convolutional neural networks (CNNs) are the prominent choice for the identification, classification, and monitoring of most medical images. In recent decades, traditional machine learning techniques such as support vector machines (SVMs) were commonly employed for biomedical image classification purposes [18]. Nevertheless, SVMs also possess certain drawbacks, such as time-consuming feature identification and extraction processes, as well as a relatively lower performance compared to several other methods. As a result, researchers in the field of biomedical image classification are now advocating for the utilization of deep learning-based approaches. While ELMs offer several advantages, they also have some challenges and limitations to consider, including the lack of iterative training and the fact that ELMs are susceptible to overfitting, especially when the number of hidden neurons is much larger than the available training samples. However, with random initialization sensitivity, the performance of ELMs can also be sensitive to the random weight initialization process. It is important to carefully consider these limitations and assess whether ELMs are the appropriate choice for a particular problem. As a matter of fact, although ELMs offer simplicity, computational efficiency, and good performance on many tasks, they may not be the ideal solution for every scenario. For example, [PERSON] et al. [19] achieved promising results in biomedical image classification by employing supervised machine learning techniques. On the Herlev dataset and the Herlev benchmark dataset, which consist of 1417 cervical cancer cells and encompass seven diagnostic classes (superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ), they achieved a classification accuracy of 0.95 with an F-value of 0.94 and a classification accuracy of 0.88 with an F-value of 0.89, respectively. These results were obtained using deep learning methods. [PERSON] et al. [20] conducted a study demonstrating that deep learning techniques can achieve diagnostic performance equivalent to experienced radiologists in diagnosing cervical cancer based on a single T2-weighted image. They utilized a deep learning model employing convolutional neural networks, specifically the Xception architecture. The model was trained with 488 images from 117 cancer patients and 509 images from 181 non-cancer patients over 50 epochs. The model's performance was evaluated using 60 images each from 60 cancer and 60 non-cancer patients. Additionally, three blinded experienced radiologists independently interpreted the same set of 120 images. The deep learning model achieved a sensitivity of 0.883, specificity of 0.933, and accuracy of 0.908. Comparatively, the radiologists exhibited a sensitivity range of 0.783-0.867, a specificity range of 0.917-0.950, and an accuracy range of 0.867-0.892. The authors in [21] proposed a method based on deep learning, which involved utilizing fuzzy C-means for MRI segmentation and extracting a set of features using discrete wavelet transform. The deep neural network architecture consisted of seven hidden layers. The results of their approach demonstrated a precision and recall of 0.97 to accurately classify normal brain MRIs and malignant ones. The researchers in [22] presented a method that leveraged convolutional neural networks consisting of seven hidden layers to detect different grades of brain cancer. They achieved an accuracy of 0.86 in accurately identifying the grades of brain cancer. [PERSON] et al. [23] addressed the same problem by employing discrete wavelet transform for feature extraction, principal component analysis for feature selection, and support vector machines for the classification task.