Recent Papers

Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by the presence of excess immature lymphocytes., Even though automation in ALL prognosis is essential for cancer diagnosis, it remains a challenge due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands that experienced pathologists read cell images carefully, which is arduous, time-consuming, and often hampered by interobserver variation. This article has automated the ALL recognition task by employing deep Convolutional Neural Networks (CNNs). The weighted ensemble of deep CNNs is explored to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates’ corresponding metrics, such as F1-score, area under the curve (AUC), and kappa values. Various data augmentations and pre-processing are incorporated to achieve a better generalization of the network. Our proposed model was trained and evaluated utilizing the C-NMC-2019 ALL dataset. The proposed weighted ensemble model has outputted a weighted F1-score of 89.7%, a balanced accuracy of 88.3%, and an AUC of 0.948 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately exhibit coarse and scatter learned areas in most cases. Since the proposed ensemble yields a better result for the aimed task, it can support clinical decisions to detect ALL patients in an early stage.

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. Nevertheless, due to having too low amplitude variation related to time on EEG signal, the proper recognition of emotion from this signal has become too challenging. Usually, considerable effort is required to identify the proper feature or feature set for an effective feature-based emotion recognition system. To extenuate the manual human effort of feature extraction, we proposed a deep machine-learning-based model with Convolutional Neural Network (CNN). At first, the one-dimensional EEG data were converted to Pearson's Correlation Coefficient (PCC) featured images of channel correlation of EEG sub-bands. Then the images were fed into the CNN model to recognize emotion. Two protocols were conducted, namely, protocol-1 to identify two levels and protocol-2 to recognize three levels of valence and arousal that demonstrate emotion. We investigated that only the upper triangular portion of the PCC featured images reduced the computational complexity and size of memory without hampering the model accuracy. The maximum accuracy of 78.22% on valence and 74.92% on arousal were obtained using the internationally authorized DEAP dataset.

Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and scatter learned areas for most example cases. Since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications.

For different controlling applications, tracking of eye movements is becoming increasingly popular nowadays. Particularly it is very helpful for the disabled people suffering from Amyotrophic Lateral Sclerosis (ALS) or other illness that prevent correct response of their limbs as well as deprive of their ability to speak. Among different eye movements tracking methods, in this paper Electrooculography (EOG) based system is developed. Electrooculogram (EOG) signals can be used to improve the communication ability as well as the quality of life of the disabled persons. Some devices are made for the purpose of lab experiments to take EOG signal for different patterns of eye movement. A typical EOG data acquisition device or circuit should be developed and compared the data to the standard device one. In this paper a low cost EOG data acquisition system is proposed. Different patterns of eye movement are recorded by the proposed EOG acquisition device and compared them with the standard BIOPAC MP36 system to check accuracy of our designed system. It will play a vital role to develop advanced devices and real life applications for paralyzed or physically disabled persons those who are affected by different diseases of any organ of the body except eye. Compared to standard device, our proposed scheme works very well with less error.

A Brain-computer Interface (BCI) is a communication pathway to provide ease to the users for interacting with the outside surroundings after translating brain signals into machine commands. The modern Steady-state Visual Evoked Potential (SSVEP) based Electroencephalographic (EEG) signals has become the most sophisticated methodology for a BCI paradigms. So, the perfection of SSVEP signal make the perfection of the BCI paradigm. The use of gel based wet electrode for the extraction of EEG signal is too much noisy and unpredictable for long time measurement which degrades the quality of SSVEP signal in a consequence degrades the performance of modern BCI paradigm. In our research, we are trying to solve this degradation of the quality of SSVEP signal. To accomplish this goal, a typical wireless BCIs using dry electrode is proposed for long term application without sacrificing Information Transfer Rate (ITR), Signal to Noise Ratio (SNR). After extracting SSVEP signal using dry electrode, Analog to Digital Conversion (ADC) is proceeded for the wireless transmission for remote BCI paradigms. Finally, after receiving this signal any BCI paradigms can be operated with high degree of accuracy.