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Acute Lymphoblastic Leukemia (ALL) is the most prevalent form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can assist in prognosis and appropriate treatment. In this study, the author proposes an approach for classifying ALL based on white blood cell images using a Convolutional Neural Network (CNN)...
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... distinctive characteristic of CNNs is their ability to organize and shape neurons into three dimensions, forming a multi-layered architecture capable of capturing intricate details and spatial relationships within images [23]. The architecture of the CNN model is depicted in Figure 2. The proposed pre-trained Inception-V3 model, as described by [24]. ...
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Thousands of individuals succumb annually to leukemia alone. This study explores the application of image processing and deep learning techniques for detecting Acute Lymphoblastic Leukemia (ALL), a severe form of blood cancer responsible for numerous annual fatalities. As artificial intelligence technologies advance, the research investigates the r...
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... Due to the high computational cost of training new algorithms with high predictive performance, we decided to use transfer learning approaches for nine different alreadypublished deep learning models-VGG16 [47], InceptionV3 [48], DensNet121 [49], Mo-bileNet [50], EfficientNetB0 [51], Xception [52], InceptionRNV2 [53], EfficientNetV2-L [54], and NASNet-L [55]-which have been shown to perform well on biomedical image classification [56][57][58][59][60][61][62][63][64]. These models were pretrained on the Imagenet database, and in this study, we used them as base models. ...
Background/Objectives: Misdiagnosing skin disorders leads to the administration of wrong treatments, sometimes with life-impacting consequences. Deep learning algorithms are becoming more and more used for diagnosis. While many skin cancer/lesion image classification studies focus on datasets containing dermatoscopic images and do not include keloid images, in this study, we focus on diagnosing keloid disorders amongst other skin lesions and combine two publicly available datasets containing non-dermatoscopic images: one dataset with keloid images and one with images of other various benign and malignant skin lesions (melanoma, basal cell carcinoma, squamous cell carcinoma, actinic keratosis, seborrheic keratosis, and nevus). Methods: Different Convolution Neural Network (CNN) models are used to classify these disorders as either malignant or benign, to differentiate keloids amongst different benign skin disorders, and furthermore to differentiate keloids among other similar-looking malignant lesions. To this end, we use the transfer learning technique applied to nine different base models: the VGG16, MobileNet, InceptionV3, DenseNet121, EfficientNetB0, Xception, InceptionRNV2, EfficientNetV2L, and NASNetLarge. We explore and compare the results of these models using performance metrics such as accuracy, precision, recall, F1score, and AUC-ROC. Results: We show that the VGG16 model (after fine-tuning) performs the best in classifying keloid images among other benign and malignant skin lesion images, with the following keloid class performance: an accuracy of 0.985, precision of 1.0, recall of 0.857, F1 score of 0.922 and AUC-ROC value of 0.996. VGG16 also has the best overall average performance (over all classes) in terms of the AUC-ROC and the other performance metrics. Using this model, we further attempt to predict the identification of three new non-dermatoscopic anonymised clinical images, classifying them as either malignant, benign, or keloid, and in the process, we identify some issues related to the collection and processing of such images. Finally, we also show that the DenseNet121 model has the best performance when differentiating keloids from other malignant disorders that have similar clinical presentations. Conclusions: The study emphasised the potential use of deep learning algorithms (and their drawbacks), to identify and classify benign skin disorders such as keloids, which are not usually investigated via these approaches (as opposed to cancers), mainly due to lack of available data.
... Rizki Firdaus Mulya et al. used the InceptionV3 model to recognise white blood cell images [12] and proposed a new method for leukaemia classification detection, which significantly improved early diagnosis accuracy. Subsequently, in 2024, Irfan Sadiq Rahat et al. evaluated the performance of several DL models on blood smear images [13] in terms of precision of automated detection and classification. ...
In recent years, computer technology has successfully permeated all areas of medicine and its management, and it now offers doctors an accurate and rapid means of diagnosis. Existing blood cell detection methods suffer from low accuracy, which is caused by the uneven distribution, high density, and mutual occlusion of different blood cell types in blood microscope images, this article introduces NBCDC‐YOLOv8: a new framework to improve blood cell detection and classification based on YOLOv8. Our framework innovates on several fronts: it uses Mosaic data augmentation to enrich the dataset and add small targets, incorporates a space to depth convolution (SPD‐Conv) tailored for cells that are small and have low resolution, and introduces the Multi‐Separated and Enhancement Attention Module (MultiSEAM) to enhance feature map resolution. Additionally, it integrates a bidirectional feature pyramid network (BiFPN) for effective multi‐scale feature fusion and includes four detection heads to improve recognition accuracy of various cell sizes, especially small target platelets. Evaluated on the Blood Cell Classification Dataset (BCCD), NBCDC‐YOLOv8 obtains a mean average precision (mAP) of 94.7%, and thus surpasses the original YOLOv8n by 2.3%.
... Additionally, we added InceptionV3 [22], which supports factorized convolutions, into our models for effective accuracy and reduced computation. Secondly, we explore this by also attempting Xception [23] (knowledgeably an adaptation of the Inception, but employing depth-wise separable convolutions to learn both performance and parameter improvements. ...
Early and accurate diagnosis for a highly aggressive hematological malignancy: Acute Lymphoblastic Leukemia. This is where automated, privacy-preserving diagnostic solutions can not only ease the burden of current diagnostic approaches but also avoid invasive, time-intensive, and prone to error. In this study, we present a Federated Learning framework for the Multi-Class classification of Acute Lymphoblastic Leukemia subtypes based on Peripheral Blood Smear images. To deal with class imbalance, data augmentation techniques were applied, and then pre-trained convolution neural networks such as InceptionV3, DenseNet121, and Xception were fine-tuned to extract features. Of these, InceptionV3 performed the best with an accuracy of 95.49% in the Federated Learning framework guaranteeing the privacy of patient data through differential privacy mechanisms. Through comparative analysis, it was confirmed that in using the Federated Learning approach, the high diagnostic accuracy and robust generalization against different datasets were preserved, while outperforming centralized learning. By proposing a scalable, privacy-compliant solution for all diagnoses, Acute Lymphoblastic Leukemia diagnoses may be transformed into the new practice of hematological oncology.
... Numerous studies have recently employed machine learning (ML)-based techniques to identify and classify leukemia in microscopic blood smear images. For example, Mulya et al. [3] utilized the CNN-V3 inception architecture to classify lymphoblast cells. Jawahar et al. [4] introduced another deep neural network, ALNett, which employs depth-wise convolution to classify leukemia cells. ...
Acute Lymphoblastic Leukemia (ALL) is a prevalent form of childhood blood cancer characterized by the proliferation of immature white blood cells that rapidly replace normal cells in the bone marrow. The exponential growth of these leukemic cells can be fatal if not treated promptly. Classifying lymphoblasts and healthy cells poses a significant challenge, even for domain experts, due to their morphological similarities. Automated computer analysis of ALL can provide substantial support in this domain and potentially save numerous lives. In this paper, we propose a novel classification approach that involves analyzing shapes and extracting topological features of ALL cells. We employ persistent homology to capture these topological features. Our technique accurately and efficiently detects and classifies leukemia blast cells, achieving a recall of 98.2% and an F1 -score of 94.6%. This approach has the potential to significantly enhance leukemia diagnosis and therapy.
... A study proposed the Inception version 3 CNN model to classify ALL using the cancer imaging archive. The model achieved an average accuracy of 0.9896 and a loss of 0.031, demonstrating high precision in classifying ALL [27]. The findings highlight the potential of CNN models like InceptionV3 to enhance the efficiency and accuracy of image-based disease diagnosis, even though it was only tested on a single dataset. ...
... Another aspect that aids in understanding these WBCs classifications is the explainability of the designed DL models, such as MobileNetV2, ResNet50, Xception, and DenseNet121 among others used in [27], which is achieved by integrating explainability and CNNs. However, these models often act as black boxes, making their decisions difficult for medical people to interpret and analyze. ...
White blood cells (WBCs) are crucial microscopic defenders of the human immune system in combating transmittable conditions triggered by germs, infections, and various other human pathogens. Timely and appropriate WBC detection and classification are decisive for comprehending the immune system’s standing and its feedback to various pathologies, assisting in diagnosing and monitoring illness. Nevertheless, the manual classification of WBCs is strenuous, extensive, and prone to errors, while automated approaches can be cost-prohibitive. Within artificial intelligence, deep learning (DL) approaches have become an appealing option for automating WBC recognition. The existing DL techniques for WBC classification face several limitations and computational difficulties, such as overfitting, limited scalability, and design complexity, often battling with function variety in WBC images and requiring considerable computational resources. This research study recommends an ingenious hybrid inception-xception Convolutional Semantic network (CNN) designed to deal with constraints in existing DL versions. The proposed network incorporates inception and depth-separable convolution layers to successfully catch attributes across many ranges, therefore minimizing concerns related to model complexity and overfitting. In contrast to traditional CNN designs, the proposed network lessens the layers made use of and increases their function removal capacities, hence enhancing the performance of WBC classification, which needs a wide variety of attribute abilities. Furthermore, the proposed model was trained, validated and tested on three popular and widely recognized datasets, namely, Leukocyte Images for Segmentation and Classification (LISC), Blood Cell Count and Detection (BCCD), and Microscopic PBS (PBS-HCB), where it demonstrates the generalization and robustness and superiority of our proposed model. The model depicted an outstanding average accuracy rate of 99.25%, 99.65%, and 98.6% on a five-fold cross-validation test for the respective datasets, surpassing existing models as detailed. The model’s robustness and superior performance, validated across diverse datasets, underscore its potential as an advanced tool for accurate and efficient WBC classification in medical diagnostics.
... The Inception v3 architecture has found widespread use across various applications [63], and is frequently employed in a "pre-trained" state from ImageNet. One notable application lies within the life sciences field, where it contributes to leukemia research [64][65][66]. ...
The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments , providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.
... The input to the Inception v3 network is an RGB image with a size of 299 × 299 pixels, which is significantly larger than the original Inception design's input size. The output layer of the network consists of 1000 units, corresponding to the 1000 classes in the ImageNet dataset [32]. ...
Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.
A precise early-stage leukemia diagnosis is essential for treating patients and saving their lives. The least expensive way for the initial diagnosis of leukemia in patients is the microscopy imaging analysis. However, this work is subjective and time-consuming. Hence, this paper creates an efficient leukemia detection model using deep learning approaches. Initially, the standard leukemia datasets are used to collect the images. The gathered images are given for the region segmentation to the Multiscale Trans-Res-Unet3+ (MTResUnet3+) Network. The segmented regions from the MTResUnet3+ are now considered for the feature extraction phase from which the most relevant attributes are mined. Here, the features like color, shape, and texture are extracted separately for performing efficient detection. Further, the features being extracted are considered for the feature selection phase, where the Election-Based Chameleon Swarm Algorithm (E-CSA) is utilized to optimally select the most appropriate features with the aim of enhancing the performance rate of the developed model. The optimally selected features are given to the next stage for detecting the presence of leukemia. Here, the Multiscale Adaptive and Attention-based Dilated Convolutional Neural Network (MAA-DCNN) is made for detecting leukemia, in which the optimization of the parameter is done with the help of hybrid E-CSA in order to elevate the detection accuracy of leukemia. The simulation analysis is performed to analyze the performance rate of the recommended leukemia detection model by contrasting it with the conventional leukemia detection models and existing algorithms using various performance metrics for validation.
