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Sarpong Kwadwo

Bio: Sarpong Kwadwo is an academic researcher. The author has contributed to research in topics: Breast cancer. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.
Topics: Breast cancer

Papers
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Journal ArticleDOI
TL;DR: This work proposes a simple convolutional neural network model trained from scratch for discriminating benign and malignant breast cancer tumors in histopathological images and explores how optimizers aid in finding good sets of parameters that help minimize loss and increase overall classification accuracy.
Abstract: Conventional approaches to breast cancer diagnosis are associated with drawbacks that ultimately affect the quality of diagnosis and subsequent treatment, pushing for the need for automatic and precise classification of breast cancer tumors. The advent of deep learning methods has witnessed an increasing interest in their applications in many tasks. The specific case of using convolutional neural networks with transfer learning has witnessed tremendous successes in many classification tasks. Nonetheless, with transfer learning, the sheer number of parameters associated with deep networks coupled with the distance disparity between source data and target data leave networks prone to overfitting, particularly in the case of limited data. Also, negative transfer may occur in the situation where the source and target domains are not related. This work proposes a simple convolutional neural network model trained from scratch for discriminating benign and malignant breast cancer tumors in histopathological images. Four deep learning optimization algorithms are leveraged and explored to ascertain how optimizers aid in finding good sets of parameters that help minimize loss and increase overall classification accuracy. By adopting a polynomial learning rate decay scheduling and implementing several

7 citations


Cited by
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Journal ArticleDOI
TL;DR: The public is urged to take action to protect themselves and their loved ones from disease-causing substances.

781 citations

Journal ArticleDOI
TL;DR: The results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.
Abstract: The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

37 citations

Journal ArticleDOI
TL;DR: This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images and proposes a class balancing framework that normalizes the class-wise confidence scores, hence effectively handling the issue of data imbalance.
Abstract: The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.

8 citations

Proceedings ArticleDOI
16 Dec 2020
TL;DR: In this article, a semi-supervised self-training scheme that utilizes self-paced learning strategy is implemented to generate and select pseudo-labeled samples to augment the training data.
Abstract: Automatically classifying skin lesion is a challenging task owing to reasons such as high intra class variations, similarities between inter-class images, occlusions in dermoscopy images that impede accurate lesion localization, not to mention data unavailability Considering that unlabeled data is abundant and cheap, this work proposes a classification framework that integrates the semi-supervised learning concepts of self-training and self-paced learning to classifying skin lesions First, accurate instance segmentation is performed using the Mask R-CNN model to effectively localize and preserve the appearance and size of the skin lesions in an image Then, a semi-supervised self-training scheme that utilizes self-paced learning strategy is implemented to generate and select pseudo-labeled samples to augment the training data The proposed framework ensures that; 1) the spatial locations of skin lesions are accurately localized and preserved, which is critical for extracting semantically meaningful information akin to classification; 2) sufficient data samples are generated to enlarge the training data to avoid overfitting; 3) a model learns both “easy” and “hard” samples during training without necessarily ignoring features from less represented classes Extensive experiments are performed using the ISIC dataset and results obtained demonstrate the effectiveness of the proposed approach

3 citations

Journal ArticleDOI
TL;DR: This study proposes a self-paced learning scheme that integrates self-training and deep learning to select and learn labeled and unlabeled data samples for classifying anterior-posterior chest images as either being pneumonia-infected or normal.
Abstract: This study proposes a self-paced learning scheme that integrates self-training and deep learning to select and learn labeled and unlabeled data samples for classifying anterior-posterior chest images as either being pneumonia-infected or normal. With this new approach, a model is first trained with labeled data. The model is evaluated on unlabeled data to generate pseudo labels for the unlabeled data. Using a novel selection scheme, the pseudo-labeled samples are then selected to update the model in next training iteration of the semi-supervised training process. The selected pseudo-labeled images to be added to the next training iteration are images with the most confident probabilities from every unlabeled class. Such a selection scheme prevents mistake reinforcement, which is a prevalent occurrence in self-training. With deep models having the tendency to latch onto well-represented class samples while ignoring less transferable and represented classes, especially in the case of unbalanced data, the proposed method utilizes a novel algorithm for the generation and selection of reliable top-K pseudo-labeled samples to be used in updating the model during the next training phase. Such an approach does not only force the model to learn the hard samples in the training data, it also helps enlarge the training set by generating enough samples that satisfy the hunger of deep models. Extensive experimental evaluation of the proposed method yields higher accuracy results compared to methods mentioned in the literature on the same dataset, an indication of the effectiveness of the proposed method.

3 citations