FIGURE 8 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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(A) Visualization of the grayscale feature maps (B) Activated colored feature maps extracted from an image of the Pro-b subtype, as depicted in Figure 1.
Source publication
Acute lymphoblastic leukemia (ALL), one of the prevalent types of carcinogenic disease, has been seen a deadly illness exposing numerous patients across the world to potential threats of lives. It impacts both adults and children providing a narrow range of chances of being cured if diagnosed at a later stage. A definitive diagnosis often demands h...
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... gradients of loss with respect to the input image pixels is calculated and the output is converted to grayscale image. The entire input data set is used for features extraction during feature extraction process, however, Figure 8(A) displays 64 grayscale feature maps extracted from an image of the Pro-b subtype, as depicted in Figure 1. These feature maps are derived from the second layer of the ResNet-152 model. ...
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... feature maps are derived from the second layer of the ResNet-152 model. If we critically review the feature maps depicted in Figure 8 it can be observed that in layer 2, network learned edges and textures. During the feature maps extraction it can also be observed that at higher layers more abstract objects detectors are learned. ...
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... the feature maps extraction it can also be observed that at higher layers more abstract objects detectors are learned. While this process gives a visual inside into the activated feature maps at layer 3 block5 as can be seen in Figure 8(B). The number of features are so high at higher level of layers that images become non interpretable. ...
Citations
... The objective of this study is to provide a complete overview of how Convolutional Neural Networks are used to diagnose medical pictures [15] [16]. They will investigate the fundamental principles of CNNs, explain their applications in various imaging modalities and clinical settings, assess current obstacles and limitations, and identify future research and development prospects in this rapidly expanding subject [17]. They can exploit AI's promise to alter medical imaging and improve patient care by learning more about CNN-based techniques. ...
Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.
... The images were resized to a standardized dimension of (227 × 227) pixels, facilitating consistent input dimensions for the subsequent stages. Normalization was applied to standardize pixel values across all images, ensuring uniformity in data representation [38]. A distinctive technique, LoGMH, was incorporated to accentuate relevant features within the images. ...
Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient’s health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet’s superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains.
