GFNB: Gini index-based Fuzzy Naive Bayes and blast cell segmentation for leukemia detection using multi-cell blood smear images.

The blood cell counting and classification ensures the evaluation and diagnosis of a number of diseases. The analysis of white blood cells (WBCs) permits us to detect the acute lymphoblastic leukemia (ALL), a type of blood cancer that causes fatality when untreated. At present, the morphological analysis of blood cells is performed manually by skilled operators, which holds numerous drawbacks. The manual techniques for leukemia detection are time-consuming and show less accurate results. Hence, there is a need for an automatic method for detecting leukemia. In order to overcome the demerits associated with the manual methods of counting and classifying, an automatic method of blast cell counting and leukemia classification is progressed. This paper proposes a leukemia detection method, using the Gini index-based Fuzzy Naive Bayes (GFNB) classifier that is the integration of Gini index and Fuzzy Naive Bayes classifier. Initially, the input multi-cell blood smear image is subjected to pre-processing, and the blast cell is segmented using the adaptive thresholding. Then, the blast cells are counted using the proposed classifier so as to decide the presence of leukemia for the effective diagnosis. Experimental analysis using the ALL-IDB1 database confirms that the proposed method operates better than the existing methods in terms of accuracy, specificity, and sensitivity that are found to be 0.9591, 0.9599, and 1, respectively. The experimental results reveal that the proposed method is reliable and accurate. Also, the proposed system can help the physicians to improve and speed up their process.Graphical abstract Leukemia is caused by the excess production of the immature leucocytes in the bone marrow that expose the human body to lose the tendency to fight against the diseases. Leukemia classification is highly needed as in the later stage, failure of the diagnosis steps may lead to the death of the person. Moreover, some countries do not have any study against the diagnosis steps of leukemia and it highly exists among the low-income people. In order to analyze the type of leukemia and to provide an effective diagnosis strategy, the paper presents a fast and highly accurate classification method. The main aim of the paper is to propose a method to perform the leukemia classification through the segmentation and classification of the WBC cells using the multi-cell blood smear images. The major steps involved in the leukemia classification are pre-processing, segmentation, feature extraction, and classification. The input blood smear image is enhanced in the pre-processing step and the pre-processed image is subjected to segmentation using the LUV color transformation and Adaptive Thresholding strategy. The features are extracted from the individual segments and they are presented to the classifier for the classification. The features extracted are shape, texture, and count of the blast cells, for which the grid-based shape extraction, local gradient pattern (LGP)-based texture features, and pixel threshold-based counting of the blast cells are employed. The proposed classifier is developed using the Gini index and Fuzzy Naive Bayes classifier.

Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images.

Acute lymphoblastic leukaemia (ALL), which is due to the malfunctioning in the bone marrow, is common among people all over the world. The haematologist suffers a lot to discriminate the presence of leukaemia in the patients using the blood smears. To overcome the inaccuracy and reliability issues, this paper proposes an automatic method of leukaemia detection, named chronological Sine Cosine Algorithm-based actor-critic neural network (Chrono-SCA-ACNN). Initially, the blood smear images are segmented using the proposed entropy-based hybrid model, from which the image-level features and statistical features are extracted from the segments. Then, the selected features are applied to the proposed classifier, which detects the leukaemia. In the proposed Chrono-SCA-ACNN, the optimal weights are selected by the proposed Chrono-SCA, which is the integration of the chronological concept in the SCA. Finally, the experimentation is performed using the ALL-IDB2 database, and the effectiveness of the proposed method over the existing methods is evaluated. From the analysis, the accuracy of the proposed method is found to be 0.99, which proves that it outperforms the existing classification methodologies. Graphical abstract Block diagram of proposed Leukaemia detection. The main aim of the paper is to segment and classify the WBCs for ALL detection in single cell blood smear images. Initially, the blood smear image is subjected to pre-processing in order to enhance the quality of the input image so as to make it effective for the further processes associated with Leukaemia detection. The pre-processed image is applied to the segmentation process that segments the cytoplasm and nucleus using the Entropy-based hybrid model. The entropy-based hybrid model is developed using the FCM and active contour to segment the cytoplasm and nucleus that is fused using the entropy. The segments are subjected to the feature extraction that extracts the statistical features and the color histogram-based features from the segments. The features are presented to the Actor-Critic Neural Network and the weights of the Neural Network (NN) are optimally tuned using the proposed Chrono-SCA. The block diagram of the proposed method of leukaemia detection is depicted in Fig. 1.

Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images.

BACKGROUND AND OBJECTIVE: Due to the development in digital microscopic imaging, image processing and classification has become an interesting area for diagnostic research. Various techniques are available in the literature for the detection of Acute Lymphocytic Leukemia from the single cell blood smear images. The purpose of this work is to develop an effective method for leukemia detection. METHODS: This work has developed deep learning based leukemia detection module from the blood smear images. Here, the detection scheme carries out pre-processing, segmentation, feature extraction and classification. The segmentation is done by the proposed Mutual Information (MI) based hybrid model, which combines the segmentation results of the active contour model and fuzzy C means algorithm. Then, from the segmented images, the statistical and the Local Directional Pattern (LDP) features are extracted and provided to the proposed Chronological Sine Cosine Algorithm (SCA) based Deep CNN classifier for the classification. RESULTS: For the experimentation, the blood smear images are considered from the AA-IDB2 database and evaluated based on metrics, such as True Positive Rate (TPR), True Negative Rate (TNR), and accuracy. Simulation results reveal that the proposed Chronological SCA based Deep CNN classifier has the accuracy of 98.7%. CONCLUSIONS: The performance of the proposed Chronological SCA-based Deep CNN classifier is compared with the state-of-the-art methods. The analysis shows that the proposed classifier has comparatively improved performance and determines the leukemia from the blood smear images.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Human subcutaneous dirofilariasis in Assam.

Human subcutaneous dirofilariasis is caused by filarial worms of the genus Dirofilaria. The parasites are transmitted to man by mosquitoes. We report three cases of human subcutaneous dirofilarias caused by Dirofilaria repens from Dibrugarh, Assam, north east India. The cases presented as subcutaneous nodules, on the chest, cheek and the anterior abdominal wall. Noting the frequency of the cases reported within 6 months, it is emphasized that subcutaneous dirofilariasis is a potentially emerging zoonosis in Assam and should be included in the differential diagnosis of patients presenting with subcutaneous nodules in Assam.

Inadvertent intracorneal injection of local anesthetic during lid surgery.

PURPOSE: To report a case of inadvertent intracorneal injection of anesthetic agents during lid anesthesia and corneal penetration without full-thickness perforation. METHOD: Case report. RESULTS: Corneal edema with acute loss of vision was noted. The patient was treated with topical, antibiotic, cycloplegic, hyperosmotic agent and lubricant. While the edema slowly subsided, a loss in endothelial cell count was noted. CONCLUSIONS: The effects of intracorneal injection of lignocaine, bupivacaine, and its preservatives have not previously been reported in the literature. A lower postinjection endothelial cell count and associated clinical features in our case indicate that endothelial toxicity occurred. This potential complication should be kept in mind with necessary precautions taken during injection of the eyelid, particularly in cases with preexisting lid laxity.

A study on refractive errors among school children in Kolkata.

Childhood visual impairment due to refractive errors is a significant problem in school children and has a considerable impact on public health. To assess the magnitude of the problem the present study was undertaken among the school children aged 5 to 10 years in Kolkata. Detailed ophthalmological examination was carried out in the schools as well as in the Regional Institute of Ophthalmology, Kolkata. Among 2317 students examined, 582 (25.11%) were suffering from refractive errors, myopia being the commonest (n = 325; 14.02%). Astigmatism affected 91 children (3.93%). There is an increase of prevalence of refractive errors with increase of age, but it is not statistically significant (p > 0.05). There is also no significant difference of refractive errors between boys and girls.

A case report of unifocal Langerhans' cell histiocytosis or eosinophilic granuloma.

A case of unifocal Langerhans' cell histiocytosis or eosinophilic granuloma in a child is reported where the frontal, zygomatic and maxillary bones of the left orbital wall are affected. As eosinophilic granuloma is a rare disease and the involvement of maxillary bone in orbital eosinophilic granuloma is not well documented in the literature, this case is reported for its unusual involvement of 3 bones at a single site. The diagnosis was established by clinical, radiological and histological findings. As no other system or site was involved and considering the osseous involvement of multiple bones at one site, local radiotherapy was preferred as the mode of treatment. The patient responded favourably to radiotherapy with reduction of proptosis and tumour mass but there was no visual recovery. In the absence of universal agreement over the mode of treatment, the result emphasises the probable benign nature of the tumour and the need to withhold more aggressive treatment modalities for extensive multisystem involvements.