Publications

2023

1. STI

   Segmentation of Hemorrhagic Areas in Human Brain from CT Scan Images

   Abu Noman Md Sakib, Nipa Anjum, and Sk. Md. Masudul Ahsan

   In 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), 2023

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   Brain hemorrhage is potentially a fatal condition that results from internal bleeding in the human brain. In this study, Computed Tomography (CT) scan images have been used for segmentation tasks to pinpoint the area of hemorrhage. Unique data augmentation techniques using non-linear transformations like, Twirl and Spherical have been used along with traditional data augmentation techniques to increase variation in the dataset. The hemorrhagic portion of the brain in images that are easy to distinguish have been annotated to perform the segmentation task. The segmentation task was applied using U-Net and U-Net++ architecture. U-Net architecture has shown 84.33% Intersection over Union (IoU) and 91.34% dice coefficient score whereas U-Net++ has achieved 17.06% IoU and 28.27% dice coefficient score after applying some non-linear transformations on the dataset.

   @inproceedings{10103333,
     author = {Md Sakib, Abu Noman and Anjum, Nipa and Ahsan, Sk. Md. Masudul},
     booktitle = {2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)},
     title = {Segmentation of Hemorrhagic Areas in Human Brain from CT Scan Images},
     year = {2023},
     publisher = {IEEE},
     address = {Dhaka, Bangladesh},
     pages = {1-5},
     doi = {10.1109/STI56238.2022.10103333},
     isbn = {978-1-6654-9045-0},
     dimensions = {true},
   }

2. ICICTD

   Classification of Brain Hemorrhage Using Deep Learning from CT Scan Images

   Nipa Anjum, Abu Noman Md. Sakib, and Sk. Md. Masudul Ahsan

   In Proceedings of International Conference on Information and Communication Technology for Development, 2023

   Abs Bib Website

   Brain hemorrhage is a life-threatening problem that happens by bleeding inside human head. In this study, computed tomography (CT) scan images have been used to classify whether the case is hemorrhage or non-hemorrhage. Different convolutional neural network (CNN) models have been observed along with some pre-trained deep learning models such as VGG16, VGG19, ResNet150, ResNet152 and InceptionV3. Pre-trained models have performed well on the dataset but all of them are heavyweight architectures in terms of number of total parameters. But the proposed model is a lightweight architecture as well as a well performing one. After evaluating the model performance, it has been observed that the proposed model gave 96.67% accuracy, 97.08% sensitivity and 96.25% specificity which is the best among other custom CNN models.

   @inproceedings{10.1007/978-981-19-7528-8_15,
     author = {Anjum, Nipa and Sakib, Abu Noman Md. and Masudul Ahsan, Sk. Md.},
     editor = {Ahmad, Mohiuddin and Uddin, Mohammad Shorif and Jang, Yeong Min},
     booktitle = {Proceedings of International Conference on Information and Communication Technology for Development},
     title = {Classification of Brain Hemorrhage Using Deep Learning from CT Scan Images},
     year = {2023},
     publisher = {Springer Nature Singapore},
     address = {Singapore},
     doi = {10.1007/978-981-19-7528-8_15},
     pages = {181-193},
     isbn = {978-981-19-7528-8},
     dimensions = {true},
   }

2021

1. WSCG

   A Deep CNN Model for Skin Cancer Detection and Classification

   Masum Shah Junayed, Nipa Anjum, Abu Noman Md Sakib, and 1 more author

   2021

   Abs

   Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection of skin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-based methods help dermatologists in classifying medical images. This paper proposes a deep learning-based model to detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, we collected a dataset that includes four skin cancer image data before applying them in augmentation techniques to increase the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data, our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNet by 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while being computationally comparable.

2020

1. ICCIT

   SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes

   Afsana Ahsan Jeny, Abu Noman Md Sakib, Masum Shah Junayed, and 3 more authors

   In 2020 23rd International Conference on Computer and Information Technology (ICCIT), 2020

   Abs Bib Website

   Skin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.

   @inproceedings{9392716,
     author = {Jeny, Afsana Ahsan and Sakib, Abu Noman Md and Junayed, Masum Shah and Lima, Khadija Akter and Ahmed, Ikhtiar and Islam, Md Baharul},
     booktitle = {2020 23rd International Conference on Computer and Information Technology (ICCIT)},
     title = {SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes},
     year = {2020},
     volume = {},
     number = {},
     pages = {1-6},
     doi = {10.1109/ICCIT51783.2020.9392716},
   }

2. IPAS

   EczemaNet: A Deep CNN-based Eczema Diseases Classification

   Masum Shah Junayed, Abu Noman Md Sakib, Nipa Anjum, and 2 more authors

   In 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), 2020

   Abs Bib Website

   Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is tough to distinguish between different types of Eczema because of the similarities in symptoms. In recent years, several attempts have been taken to automate the detection of skin diseases with much accuracy. Many methods such as Image Processing Techniques, Machine Learning algorithms are getting used to execute segmentation and classification of skin diseases. It is found that among all those skin disease detection systems, particularly detection work on eczema disease is rare. There is also insufficiency in eczema disease dataset. In this paper, we propose a novel deep CNN-based approach for classifying five different classes of Eczema with our collected dataset. Data augmentation is used to transform images for better performance. Regularization techniques such as batch normalization and dropout helped to reduce overfitting. Our proposed model achieved an accuracy of 96.2%, which exceeded the performance of the state of the arts.

   @inproceedings{9334929,
     author = {Junayed, Masum Shah and Sakib, Abu Noman Md and Anjum, Nipa and Islam, Md Baharul and Jeny, Afsana Ahsan},
     booktitle = {2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS)},
     title = {EczemaNet: A Deep CNN-based Eczema Diseases Classification},
     year = {2020},
     volume = {},
     number = {},
     pages = {174--179},
     doi = {10.1109/IPAS50080.2020.9334929},
   }