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15 results on '"Jabeen, Kiran"'

1. A Fusion Framework of Pre-trained Deep Learning Models for Oral Squamous Cell Carcinoma Classification

Author

Khan, Muhammad Attique, Mir, Momina, Ullah, Muhammad Sami, Hamza, Ameer, Jabeen, Kiran, Gupta, Deepak, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Fortino, Giancarlo, editor, Kumar, Akshi, editor, Swaroop, Abhishek, editor, and Shukla, Pancham, editor

Published
2024
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5. A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.

Author

Jabeen, Kiran, Khan, Muhammad Attique, Hameed, Mohamed Abdel, Alqahtani, Omar, Alouane, M. Turki-Hadj, and Masood, Anum

Subjects
CONVOLUTIONAL neural networks, TUMOR classification, BREAST cancer, MAMMOGRAMS, OPTIMIZATION algorithms
Abstract

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease's influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue's nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named IN breast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework's higher accuracy and precision rate. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Effect of early tranexamic acid administration on mortality, hysterectomy, and other morbidities in women with post-partum haemorrhage (WOMAN): an international, randomised, double-blind, placebo-controlled trial

Author

Shakur, Haleema, Roberts, Ian, Fawole, Bukola, Chaudhri, Rizwana, El-Sheikh, Mohamed, Akintan, Adesina, Qureshi, Zahida, Kidanto, Hussein, Vwalika, Bellington, Abdulkadir, Abdulfetah, Etuk, Saturday, Noor, Shehla, Asonganyi, Etienne, Alfirevic, Zarko, Beaumont, Danielle, Ronsmans, Carine, Arulkumaran, Sabaratnam, Grant, Adrian, Afsana, Kaosar, Gülmezoglu, Metin, Hunt, Beverley, Olayemi, Oladapo, Chalmers, Iain, Lumbiganon, Pisake, Piaggio, Gilda, Brady, Tony, Elbourne, Diana, Balogun, Eni, Pepple, Tracey, Prowse, Danielle, Quashi, Nigel, Barneston, Lin, Barrow, Collette, Cook, Lisa, Frimley, Lauren, Gilbert, Daniel, Gilliam, Catherine, Jackson, Rob, Kawahara, Taemi, Miah, Hakim, Kostrov, Sergey, Ramos, Maria, Edwards, Phil, Godec, Tom, Huque, Sumaya, Okunade, Olujide, Adetayo, Olusade, Kayani, Aasia, Javaid, Kiran, Biryabarema, Chrstine, Tchounzou, Robert, Regmi, Mohan, Dallaku, Kastriot, Sahani, Mateus, Akhter, Sayeba, Meda, Nicolas, Dah, Anthony Kwame, Odekunle, Olufemi, Monehin, Oluwabusola, Ojo, Austin, Akinbinu, Grace, Offiah, Ifeoma, Akpan, Ubong, Udofia, Uduak, Okon, Useneno, Omoronyia, Ezukwa, James, Okpe, Bello, Nike, Adeyemi, Blessed, Aimakhu, Chris, Akinsanya, Olufemi, Adeleye, Bamidele, Adeyemi, Oluwaseun, Oluwatosin, Kayode, Aboyeji, Abiodun, Adeniran, Abiodun, Adewale, Adebayo, Olaomo, Noah, Omo-Aghoja, Lawrence, Okpako, Emmanuel, Oyeye, Lucky, Alu, Francis, Ogudu, John, Ladan, Ezekiel, Habib, Ibrahim, Okusanya, Babasola, Onafowokan, Olatunde, Isah, David, Aye, Abalaka, Okogbo, Felix, Aigere, Egbaname, Ogbiti, Mark, Onile, Temitope, Salau, Olaide, Amode, Yinka, Shoretire, Kamil, Owodunni, Adebola, Ologunde, Kehinde, Ayinde, Akintunde, Alao, Moses, Awonuga, Olalekan, Awolaja, Babatunde, Adegbola, Omololu, Habeebu-Adeyemi, Fatimah, Okunowo, Adeyemi, Idris, Hadiza, Okike, Ola, Madueke, Nneka, Mutihir, Josiah, Joseph, Nankat, Adebudo, Babatunde, Fasanu, Adeniyi, Akintunde, Olugbenga, Abidoye, Olufemi, Opreh, Owigho, Udonwa, Sophia, Dibia, Gladys, Bazuaye, Simeon, Ifemeje, Arafat, Umoiyoho, Aniefiok, Inyang-Etoh, Emmanuel, Yusuf, Sununu, Olayinka, Kayode, Adeyemi, Babalola, Ajenifuja, Olusegun, Ibrahim, Umar, Adamu, Yusuf Baffah, Akinola, Oluwarotimi, Adekola-Oni, Grace, Kua, Paul, Iheagwam, Roseline, Idrisa, Audu, Geidam, Ado, Jogo, Andrea, Agulebe, Joseph, Ikechebelu, Joseph, Udegbunam, Onyebuchi, Awoleke, Jacob, Adelekan, Oluseyi, Sulayman, Hajaratu, Ameh, Nkeiruka, Onaolapo, Nurudeen, Adelodun, Affiss, Golit, William, Audu, Dachollom, Adeniji, Adetunji, Oyelade, Folasade, Dattijo, Lamaran, Henry, Palmer, Loto, Olabisi, Umeora, Odidika, Onwe, Abraham, Nzeribe, Emily, Okorochukwu, Bartthy, Adeniyi, Augustine, Gbejegbe, Emmanuel, Ikpen, Akpojaro, Nwosu, Ikemefuna, Sambo, Abdulrasaq, Ladipo, Olubunmi, Abubakar, Sola, Okike, Ola Nene, Nduka, Enyinnaya Chikwendu, Ezenkwele, Eziamaka Pauline, Onwusulu, Daniel, Irinyenikan, Theresa Azonima, Singh, Swati, Bariweni, Amaitari, Galadanci, Hadiza, Achara, Peter, Osayande, Osagie, Gana, Mohammed, Jabeen, Kiran, Mobeen, Ayesha, Mufti, Sadaf, Zafar, Maliha, Ahmad, Basharat, Munawar, Maimoona, Gul, Jeharat, Usman, Naseema, Shaheen, Fehmida, Tariq, Mariam, Sadiq, Nadia, Batool, Rabia, Ali, Habiba Sharaf, Jaffer, Manahil, Baloch, Asma, Mukhtiar, Noonari, Ashraf, Tasneem, Asmat, Raheela, Khudaidad, Salma, Taj, Ghazala, Qazi, Roshan, Dars, Saira, Sardar, Faryal, Ashfaq, Sanobar, Majeed, Saeeda, Jabeen, Sadaqat, Karim, Rukhsana, Burki, Farzana, Bukhari, Syeda Rabia, Gul, Fouzia, Jabeen, Musarrat, Sherin, Akhtar, Ain, Qurratul, Rao, Shahid, Shaheen, Uzma, Manzoor, Samina, Masood, Shabween, Rizvi, Shabana, Ali, Anita, Sajid, Abida, Iftikhar, Aisha, Batool, Shazia, Dar, Lubna, Sohail, Shahenzad, Rasul, Shazia, Humayun, Shamsa, Sultana, Rashida, Manzoor, Sofia, Mazhar, Syeda, Batool, Afshan, Nazir, Asia, Tasnim, Nasira, Masood, Hajira, Khero, Razia, Surhio, Neelam, Aleem, Samana, Israr, Naila, Javed, Saba, Bashir, Lubna, Iqbal, Samina, Aleem, Faiza, Sohail, Rubina, Iqbal, Saima, Dojki, Samina, Bano, Alia, Saba, Naseem, Hafeez, Maimoona, Akram, Nishat, Shaheen, Riffat, Hashmi, Haleema, Arshad, Sharmeen, Hussain, Rubina, Khan, Sadia, Shaheen, Nighat, Khalil, Safia, Sachdev, Pushpa, Arain, Gulfareen, Zarreen, Amtullah, Saeed, Sara, Hanif, Shamayela, Tariq, Nabia, Jamil, Mahwish, Chaudhry, Shama, Rajani, Hina, Wasim, Tayyiba, Aslam, Summera, Mustafa, Nilofar, Quddusi, Huma, Karim, Sajila, Sultana, Shazia, Harim, Misbah, Chohan, Mohd, Salman, Nabila, Waqar, Fareesa, Sadia, Shamsunnisa, Kahloon, Lubna, Manzoor, Shehla, Amin, Samar, Akram, Umbreen, Ikram, Ambreen, Kausar, Samina, Batool, Tahira, Naila, Brigadier, Kyani, Tahir, Biryabarema, Christine, Bulime, Ruth, Akello, Regina, Lwasa, Bernadette Nakawooya, Ayikoru, Joselyn, Namulwasira, Christine, Komagum, Patrick, Rebecca, Isabirye, Annet, Nayiga, Nuulu, Nakirigya, Nionzima, Elizabeth, Bwotya, Rose, Nankya, Margret, Babirye, Sarah, (RIP), Ngonzi, Joseph, Sanchez, Cesar, Innocent, Nkonwa, Anitah, Kusasira, Jackson, Ayiko, Ndagire, Elizabeth, Nanyongo, Christine, Drametu, Dominic, Meregurwa, Grace, Banya, Francis, Atim, Rita, Byaruhanga, Emmanuel, Felix, Lema, Iman, Hussein, Oyiengo, Vincent, Waigi, Peninah, Wangui, Rose, Nassir, Faiza, Soita, Musimbi, Msengeti, Rophina, Zubier, Zeinab, Mabeya, Hillary, Wanjala, Antony, Mwangi, Henry, Liyayi, Brian, Muthoka, Evelyn, Osoti, Alfred, Otara, Amos, Ongwae, Veronicah, Wanjohi, Victor, Musila, Bonface, Wekesa, Kubasu, Bosire, Alex Nyakundi, Ntem, Alice, Njoache, Angeline, Ashu, Alice, Simo, André, Keka, Dorothy, Bruno, Kenfack, Ndouoya, Amadou, Saadio, Martin, Tchana, Mesack, Gwan, Odel, Assomo, Pauline, Mutsu, Venantius, Eric, Nji, Foumane, Pascal, Nsem, Philemon, Fouedjio, Jeanne, Fouelifack, Ymele, Tebeu, Pierre Marie, Nko'ayissi, Georges, Mbong, Eta Ngole, Nabag, Wisal, Desougi, Riham, Mustafa, Hadia, Eltaib, Huida, Umbeli, Taha, Elfadl, Khalid, Ibrahim, Murwan, Mohammed, Abdalla, Ali, Awadia, Abdelrahiem, Somia, Musa, Mohammed, Awadalla, Khidir, Ahmed, Samirra, Bushra, Mahdi, Babiker, Omer, Abdullahi, Hala, Ahmed, Mohamed, Safa, Elhassan, Almardi, Huida, Rayis, Duria, Abdelgabar, Saeed Abdelrahman, Houghton, Gillian, Sharpe, Andrew, Thornton, Jim, Grace, Nick, Smith, Carys, Hinshaw, Kim, Edmundson, Dawn, Ayuk, Paul, Bates, Alison, Bugg, George, Wilkins, Joanne, Tower, Clare, Allibone, Alysha, Oteng-Ntim, Eugene, Kazumari, Ahmad, Danford, Anna, Ngarina, Matilda, Abeid, Muzdalifat, Mayumba, Khadija, Zacharia, Magreth, Mtove, George, Madame, Leonard, Massinde, Anthony, Mwambe, Berno, Onesmo, Rwakyendela, Ganyaka, Sebastian Kitengile, Gupta, Shyam, Bhatt, Rabindra, Agrawal, Ajay, Pradhan, Pramila, Dhakal, Nikita, Yadav, Punita, Karki, Gyanendra, Shrestha, Bhola Ram, Lubeya, Mwansa, Mumba, Jane, Silwimba, Willies, Hansingo, Isaiah, Bopili, Noojiri, Makukula, Ziche, Kawimbe, Alexander, Lubeya, Mwansa Ketty, Mtambo, Willard, Ng'ambi, Mathew, Cenameri, Saimir, Tasha, Ilir, Kruja, Aferdita, Brahimaj, Besnik, Tola, Armida, Kaza, Leon, Tshombe, Desire, Buligho, Elizabeth, Paluku-Hamuli, Roger, Kacha, Charles, Faida, Kato, Musau, Badibanga, Kalyana, Herman, Simisi, Phanny, Mulyumba, Serge, Jason, Nzanzu Kikuhe, Lubamba, Jean Robert, Missumba, Willis, Islam, Ferdousi, Begum, Nazneen, Chowdhury, Ferdousi, Begum, Rokeya, Basher, Farjana, Nargis, Nazlima, Kholdun, Abu, Jesmin, Shahela, Paul, Shrodha, Segni, Hailemariam, Ayana, Getachew, Haleke, William, Hussien, Hassen, Geremew, Fikre, Bambara, Moussa, Somé, Adolphe, Ly, Amadou, Pabakba, Roamba, Fletcher, Horace, Samuels, Leslie, Opare-Addo, Henry, Larsen-Reindorf, Roderick, Nyarko-Jectey, Kwadwo, Mola, Glen, Wai, Malts, El Rahman, Magdy, Basta, Wafaa, Khamis, Hussein, Escobar, Maria Fernanda, Vallecilla, Liliana, and Faye, Gabriel Essetchi

Published
2017
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9. Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Author

Rauf, Fatima, Khan, Muhammad Attique, Bashir, Ali Kashif, Jabeen, Kiran, Hamza, Ameer, Alzahrani, Ahmed Ibrahim, Alalwan, Nasser, Masood, Anum, Rauf, Fatima, Khan, Muhammad Attique, Bashir, Ali Kashif, Jabeen, Kiran, Hamza, Ameer, Alzahrani, Ahmed Ibrahim, Alalwan, Nasser, and Masood, Anum

Abstract

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

Published
2023

11. BC 2 NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection.

Author

Jabeen, Kiran, Khan, Muhammad Attique, Balili, Jamel, Alhaisoni, Majed, Almujally, Nouf Abdullah, Alrashidi, Huda, Tariq, Usman, and Cha, Jae-Hyuk

Subjects
*FEATURE selection, *DEEP learning, *TUMOR classification, *MAMMOGRAMS, *ARTIFICIAL intelligence
Abstract

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters' initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework. [ABSTRACT FROM AUTHOR]

Published
2023
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15. Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Author

Rauf F, Khan MA, Bashir AK, Jabeen K, Hamza A, Alzahrani AI, Alalwan N, and Masood A

Abstract

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rauf, Khan, Bashir, Jabeen, Hamza, Alzahrani, Alalwan and Masood.)

Published
2023
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