Sabrina Dhalla’s research while affiliated with Panjab University and other places

Leukemia is one of the deadly cancers which spreads itself at an exponential rate and has a detrimental impact on leukocytes in the human blood. To automate the process of leukemia detection, researchers have utilized deep learning networks to analyze blood smear images. In our research, we have proposed the usage of networks that mimic the human brain’s real working. These models are fed features from numerous convolution layers, each having its own set of additional skip connections. It is then stored and processed as vectors, making them rotationally invariant as well, a characteristic not found in other deep learning networks, specifically convolutional neural networks (CNNs). The network is then pruned by 20% to make it more deployable in resource-constrained environments. This research also compares the model’s performance by four ablation experiments and concludes that the proposed model is optimal. It has also been tested on three different types of datasets to highlight its robustness. The average values of all three datasets correspond to specificity: 96.97%, sensitivity: 96.81%, precision: 96.79% and accuracy: 97.44%. In a nutshell, the outcomes of the proposed model, i.e., PrunedResCapsNet make it more dynamic and effective compared with other existing methods.

Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

Leukemia, a type of blood cancer, is amongst the most deadly cancers worldwide. Since it affects leukocytes in the bloodstream, fast and early detection of abnormal leukocytes is required. Thus, precise detection of leukemia highly relies on accurate segmentation of leukocytes from blood smear images. The segmentation process has become quite robust with the development of deep neural networks, especially convolutional neural networks (CNNs). Such models have also shown superior results compared to traditional machine learning algorithms. This work represents a deep learning-based encoder–decoder model that focuses on salient multiscale leukocyte features. It is accomplished by combining features derived from standard and dilated convolutions. Using a convolutional block attention module (CBAM) in the network facilitates the extraction of refined features. We evaluated the performance of the proposed approach by conducting ablation studies on three publicly available datasets: ALL_IDB1, CellaVision and LISC. The first study is conducted to finalize the architecture of the dilated encoder path. In the subsequent study, a series of experiments are performed to obtain the most effective attention module. The last set of experiments deals with only a single encoder path that encapsulates dilated convolutions’ importance. The resultant values of the proposed method are also compared with the state-of-the-art techniques using four performance indices: Dice score, IoU, PPV and NPV and qualitatively by visual results.

Purpose: Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose the existence of leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. Methods: To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses marker-based watershed algorithm and peak local maxima. Results: The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was same i.e. 94% but Structural Similarity Index Metric (SSIM) and recall of HSV was better than other two. Conclusion: The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

Leukemia, the cancer of blood-forming tissues, becomes fatal if not detected in the early stages. It is detected through a blood smear test that involves the morphological analysis of the stained blood slide. The manual microscopic examination of slides is tedious, time-consuming, error-prone, and subject to inter-observer and intra-observer bias. Several computerized methods to automate this task have been developed to alleviate these issues during the past few years. However, no exclusive comprehensive review of these methods has been presented to date. Such a review shall be highly beneficial for novice readers interested in pursuing research in this domain. This paper fills the void by presenting a comprehensive review of 149 papers detailing the methods used to analyze blood smear images and detect leukemia. The primary focus of the review is on presenting the underlying techniques used, their reported performance, along with their merits and demerits. It also enumerates the research issues that have been satisfactorily solved and open challenges still existing in the domain.

Segmentation of blood cells is a prerequisite step in automated morphological analysis of blood smear images, cell count determination and diagnosis of various diseases such as leukemia. It is extremely challenging due to different sizes, shapes, morphological characteristics and overlapping of blood cells. Due to its complicated nature, it is generally performed as a sequence of steps. However, sequential segmentation results in restricted accuracy due to cascading of errors that creep during each stage. On the contrary, pixel-wise segmentation of blood cells is a single step task and gives promising results. In this paper, we propose LeukoSegmenter, a double encoder-decoder for precise pixel-wise segmentation of leukocytes from blood smear images. It uses pre-trained ResNet18 based encoders and U-Net based decoders. Feature maps obtained from the first network are utilised as attention maps. These are used as input in conjunction with the original 3-channel image to obtain final mask from the second network. This mechanism allows the latter encoder-decoder pair to focus explicitly on leukocytes and ignore other blood cells and debris, thus improving the segmentation accuracy. Experiments on ALL-IDB1 dataset show that the proposed LeukoSegmenter achieves intersection-over-union score of 94.6827% and Dice score of 97.1987% which is superior than that of state-of-the-art methods.

Acute Lymphoblastic Leukemia (ALL) is one of the most commonly occurring type of leukemia which poses a serious threat to life. It severely affects White Blood Cells (WBCs) of the human body that fight against any kind of infection or disease. Since, there are no evident morphological changes and the signs are pretty similar to other disorders, it becomes difficult to detect leukemia. Manual diagnosis of leukemia is time-consuming and is even susceptible to errors. Thus, in this paper, computer assisted diagnosis method has been implemented to detect leukemia using deep learning models. Three models namely, VGG11, ResNet18 and ShufflenetV2 have been trained and fine tuned on ISBI 2019 C-NMC dataset. Finally an ensemble using weighted averaging technique is formed and evaluated as per the criteria of binary classification. The proposed method gave an overall accuracy of 87.52% and F1-score of 87.40%. Thus, it outperforms most of the existing techniques for the same dataset.